An intelligent Scala code generation method and system based on artificial intelligence

Abstract

The application provides an intelligent Scala code generation method and system based on artificial intelligence, which comprises the following steps: obtaining a user natural language requirement description, performing semantic understanding and information extraction, extracting function, input and output type and constraint condition to form standardized technical elements, and mapping to a Scala syntax and programming paradigm to generate a requirement semantic vector; performing static analysis on a project code library, extracting code structure, dependency relationship and coding style, constructing a multi-level context knowledge graph, and obtaining a context vector through a graph neural network and an attention mechanism; fusing the two types of vectors to generate a joint vector, relying on a pre-trained Scala code generation model to generate a code framework and core code, generating and optimizing a candidate scheme through a beam search algorithm, and determining an optimal code scheme; performing formatting, type checking, performance optimization and test verification on the optimal code scheme, automatically generating document annotations, and obtaining a final perfect code. The application realizes perfect balance between performance and reliability.

Description

Technical Field

[0001] This invention relates to the field of software development, specifically to an intelligent Scala code generation method and system based on artificial intelligence, belonging to the field of intelligent programming assistance technology. Background Technology

[0002] In the field of software development, especially in development environments for complex programming languages, code generation tools have been widely used as a key technology to improve development efficiency. With the rapid development of big data, distributed computing, and functional programming, Scala has become one of the preferred languages ​​for developing high-performance, scalable applications due to its combination of object-oriented and functional programming features, as well as its seamless integration with the Java ecosystem.

[0003] Currently, code generation technologies mainly include template-based code generation systems and rule-based code conversion tools. For example, some IDEs have built-in code template systems that allow developers to define commonly used code snippet templates and quickly insert them while programming; while another type of tool, such as a code converter, can convert the code structure of one language into an equivalent implementation in the target language.

[0004] In recent years, general-purpose code generation technologies based on deep learning (such as GitHub Copilot and Codex) have been able to generate code snippets based on context and comments by learning from a large number of open-source code repositories. These technologies employ large language models to generate grammatically correct code by understanding input prompts. However, most of these tools are general-purpose solutions and are not specifically optimized for the syntax features and programming paradigms of the Scala language, making it difficult to handle the complex features unique to Scala, such as higher-order functions, pattern matching, and implicit conversions.

[0005] The existing technical solutions have the following main technical problems: First, they lack a deep understanding of the specific syntax and paradigms of the Scala language, which makes it impossible for the generated code to make full use of the features of the Scala language; second, they cannot effectively perceive the project context, including key information such as project dependency libraries, code style and architectural patterns, and the generated code is often inconsistent with the style of the existing project. Summary of the Invention

[0006] The purpose of this invention is to overcome the problems existing in the prior art and provide an intelligent Scala code generation method and system based on artificial intelligence, which can deeply understand the natural language requirements input by users and generate high-quality code that conforms to the characteristics of the Scala language and the coding style of the project, combined with the project context.

[0007] This invention provides an intelligent Scala code generation method based on artificial intelligence, comprising:

[0008] Obtain the user's input natural language requirement description, perform semantic understanding and information extraction on the natural language requirement description, extract functional description, input and output types and constraints, obtain a standardized set of technical elements, and map the standardized set of technical elements to the syntax structure and programming paradigm of the Scala language to obtain a requirement semantic vector;

[0009] Static analysis is performed on the project codebase to extract the project code structure, dependencies and coding style, construct a multi-level context knowledge graph, and process the multi-level context knowledge graph using graph neural networks and attention mechanisms to obtain context vector representations;

[0010] The requirement semantic vector and the context vector representation are fused to obtain a joint vector representation. Based on the joint vector representation, a pre-trained Scala code generation model is used to generate a code framework and core code. Multiple candidate code implementation schemes are generated through a beam search algorithm, and the optimal scheme is selected to obtain the optimal code scheme.

[0011] The optimal code solution is then formatted, type-checked, performance-optimized, and tested. Documentation comments are automatically generated to obtain the final, complete code.

[0012] Furthermore, the process of obtaining the user's input natural language requirement description involves semantic understanding and information extraction from the natural language requirement description, extracting functional descriptions, input / output types, and constraints to obtain a standardized set of technical elements. This standardized set of technical elements is then mapped to the syntax structure and programming paradigm of the Scala language to obtain a requirement semantic vector, including:

[0013] Obtain the user's input natural language requirement description, and perform text cleaning, word segmentation, part-of-speech tagging and named entity recognition on the natural language requirement description to obtain a standardized text representation;

[0014] Based on the standardized text representation, semantic encoding is performed using a pre-trained BERT model, and the user's core development intent is identified through an intent classifier. At the same time, the subject, behavior, and modifier relationships in the requirements are extracted through dependency parsing to obtain a structured intent representation.

[0015] Based on the structured intent representation, an information extraction model is used to extract three types of technical information from the structured intent representation: functional description, input / output type, and constraints, to obtain a standardized set of technical elements.

[0016] Based on the functional descriptions and constraints in the standardized set of technical elements, the standardized set of technical elements is mapped to the specific syntax structure and programming paradigm of the Scala language to obtain the mapped Scala semantic representation.

[0017] The mapped Scala semantic representation is vector-encoded using the CodeBERT code pre-trained model to obtain the requirement semantic vector. The BERT model is used to extract the structured intent in the natural language requirement, and the CodeBERT model is used to vector-encode the mapped Scala semantic representation. The combination of the BERT model and the CodeBERT model can realize cross-modal feature extraction from natural language to programming language semantics.

[0018] Furthermore, based on the structured intent representation, an information extraction model is used to extract three types of technical information—functional description, input / output type, and constraints—from the structured intent representation to obtain a standardized set of technical elements, including:

[0019] Based on the structured intent representation, a specially trained information extraction model is used to identify keywords describing functions, extract function descriptions of ranking algorithms or data filtering, and obtain a set of function descriptions.

[0020] Based on the structured intent representation, the information extraction model is used to identify type-related semantic components, extract the correspondence between input types and output types, and obtain a set of input and output types.

[0021] Based on the structured intent representation, the information extraction model is used to identify semantic components related to performance constraints and security constraints, extract thread safety requirements and time complexity limits, and obtain a set of constraints.

[0022] The set of functional descriptions, the set of input / output types, and the set of constraints are structurally integrated to obtain the standardized set of technical elements.

[0023] Further, based on the functional descriptions and constraints in the standardized set of technical elements, the standardized set of technical elements is mapped to the specific syntax structure and programming paradigm of the Scala language to obtain the mapped Scala semantic representation; the mapped Scala semantic representation is then vector-encoded using the CodeBERT code pre-training model to obtain the requirement semantic vector, including:

[0024] Based on the functional descriptions in the standardized set of technical elements, the corresponding specific syntax structures of the Scala language are identified, sorting intentions are mapped to Scala collection operation methods, asynchronous processing intentions are mapped to Future or Actor models, and data transformation intentions are mapped to map or flatMap operations to obtain the syntax structure mapping results.

[0025] Based on the constraints in the standardized set of technical elements, the corresponding Scala programming paradigm is identified, and thread safety requirements are mapped to immutable data structures or synchronization mechanisms, while performance requirements are mapped to tail recursion optimization or parallel sets, thus obtaining the programming paradigm mapping result.

[0026] The syntactic structure mapping result and the programming paradigm mapping result are integrated to form a complete Scala semantic representation, resulting in the integrated Scala semantic representation;

[0027] Using the CodeBERT code pre-trained model, feature extraction and vector encoding are performed on the integrated Scala semantic representation to generate a 768-dimensional semantic vector, thus obtaining the required semantic vector.

[0028] Furthermore, the static analysis of the project codebase extracts the project code structure, dependencies, and coding style, constructs a multi-layered context knowledge graph, and processes the multi-layered context knowledge graph using graph neural networks and attention mechanisms to obtain context vector representations, including:

[0029] Static code analysis tools are used to construct abstract syntax trees for the project codebase, extracting classes, traits, objects, and methods in the project and their corresponding relationships to obtain a structured representation of the project code.

[0030] Parse the project's build configuration file, extract the external libraries that the project depends on and their corresponding version information, and analyze the usage patterns of these external libraries in the project to obtain the project dependency graph;

[0031] Analyze the existing code of the project, extract coding style features and common programming patterns, and obtain the project's unique style fingerprint;

[0032] The structured representation of the project code, the project dependency graph, and the project-specific style fingerprint are structurally integrated to construct a multi-layered contextual knowledge graph that includes a code structure layer, a dependency layer, and a style pattern layer.

[0033] The multi-layered context knowledge graph is used to extract node features and pass messages, and the relevance of information at each level to the current task is calculated and weighted by an attention mechanism to obtain the context vector representation.

[0034] Furthermore, the step of using a graph neural network to extract node features and perform message passing on the multi-layered context knowledge graph, and calculating the relevance of information at each level to the current task through an attention mechanism and performing weighted fusion to obtain the context vector representation includes:

[0035] Based on the multi-level context knowledge graph, an input representation of a graph neural network is constructed. The nodes in the multi-level context knowledge graph are encoded as feature vectors, and the edges are encoded as adjacency matrices to obtain a graph-structured input.

[0036] A graph neural network is used to perform multi-layer message passing and feature aggregation on the graph structured input. The representation of each node is updated by aggregating the features of neighboring nodes to obtain the node update representation.

[0037] Based on the node update representation, the attention mechanism is used to calculate the attention weights of each layer of information in the code structure layer, dependency layer, and style pattern layer with the current task, and the layer weight distribution is obtained.

[0038] Based on the hierarchical weight distribution, the node representations of each level of information are weighted and summed to highlight the contextual features related to the current task, thus obtaining the context vector representation.

[0039] Further, the process involves fusing the requirement semantic vector and the context vector representation to obtain a joint vector representation, and then using a pre-trained Scala code generation model to generate a code framework and core code based on this joint vector representation. Multiple candidate code implementation schemes are generated using a beam search algorithm, and the optimal scheme is selected to obtain the optimal code scheme. This includes:

[0040] Based on the demand semantic vector and the context vector representation, the correlation between the demand semantic vector and the context vector representation is calculated through a cross-attention mechanism to obtain the correlation weight, and vector fusion is performed according to the correlation weight to obtain the joint vector representation;

[0041] Based on the joint vector representation, a pre-trained Scala code generation model using the Transformer architecture is loaded and initialized. The pre-trained Scala code generation model is used to generate a code framework including method signatures, class definition structures and import statements according to the functional intent and input / output type information in the joint vector representation, thus obtaining the code skeleton.

[0042] Based on the functional descriptions and constraints in the code skeleton and the joint vector representation, and combined with the syntax features and programming paradigms of the Scala language, the core code that implements the main function is generated, resulting in the functional code.

[0043] The beam search algorithm is used to initialize K generation paths for the functional code and maintain multiple generation paths through iterative expansion and probability evaluation. K candidate code implementation schemes are generated according to the probability distribution of the language model, resulting in a set of candidate schemes.

[0044] For each candidate code implementation scheme in the candidate scheme set, calculate the language model probability, type matching degree and performance score, and calculate the comprehensive score based on the preset weight coefficient. Select the scheme with the highest comprehensive score to obtain the optimal code scheme.

[0045] Further, based on the joint vector representation, a pre-trained Scala code generation model using the Transformer architecture is loaded and initialized. The pre-trained Scala code generation model is then used to generate a code framework including method signatures, class definition structures, and import statements based on the functional intent and input / output type information in the joint vector representation, resulting in a code skeleton, including:

[0046] Based on the joint vector representation, a pre-trained Scala code generation model using the Transformer architecture is loaded, and the model parameters of the pre-trained Scala code generation model are adjusted through supervised learning on the Scala codebase to complete the model initialization and obtain the initialized generation model.

[0047] Extract functional intent information from the joint vector representation, and use the initialized generative model to generate method signatures or class definition structures to obtain the code structure framework;

[0048] The input and output type information is extracted from the joint vector representation, and the parameter list and return type are determined using the initialized generative model based on the input and output type information to obtain the type-labeled method signature;

[0049] Based on the dependency information in the joint vector representation, the necessary import statements are generated using the initialized generative model to obtain a set of import statements.

[0050] The code skeleton is obtained by integrating the code structure framework, the method signature of the type annotation, and the set of import statements.

[0051] Furthermore, the beam search algorithm is employed to initialize K generation paths for the functional code and maintain multiple generation paths through iterative expansion and probability evaluation. Based on the probability distribution of the language model, K candidate code implementation schemes are generated, resulting in a candidate scheme set, including:

[0052] Based on the aforementioned functional code, initialize the beam search algorithm, set the beam width to K, create K initial generation paths, and obtain the initial path set;

[0053] For each generated path in the initial path set, the probability distribution of the next code tag is calculated using the pre-trained Scala code generation model to obtain the tag probability distribution;

[0054] Based on the label probability distribution, several candidate labels with the highest probability are selected for each generated path to expand the path, resulting in an expanded path set.

[0055] Calculate the cumulative probability of each path in the expanded path set, select the K paths with the highest cumulative probability as the current retained paths, and obtain the updated path set;

[0056] Repeat the calculation of the labeled probability distribution, the path expansion based on the labeled probability distribution, and the path selection based on the cumulative probability until all retained paths generate complete code implementations or reach the maximum length limit, thus obtaining the candidate solution set.

[0057] Furthermore, before performing static analysis on the project codebase to extract the project code structure, dependencies, and coding style, constructing a multi-layered context knowledge graph, and processing the multi-layered context knowledge graph using graph neural networks and attention mechanisms to obtain the context vector representation, the process further includes:

[0058] Based on the structured representation of the project codebase, a code dependency graph is constructed. The vertices of the code dependency graph represent classes, traits, objects, and methods, and the edges represent call, inheritance, and containment relationships. Each edge is assigned a weight that reflects the strength of the dependency, resulting in a weighted code dependency graph.

[0059] A fixed-parameter algorithm is applied to the weighted code dependency graph. Tree decomposition technique is used to decompose the weighted code dependency graph into a tree structure to limit the range of nodes that need to be traversed. Dynamic programming is used to accelerate the solution of subproblems to find vertex multi-way cut sets. The vertex multi-way cut set is a set of key nodes that can divide the weighted code dependency graph into multiple unconnected components that do not contain preset terminal nodes after removal.

[0060] Based on the vertex multi-way cut set, identify the connected subgraphs in the weighted code dependency graph that are not segmented by the vertex multi-way cut set, enumerate the combinations of the connected subgraphs, and evaluate the cohesion and complexity of each connected subgraph through information entropy and modularity index to obtain the set of uncut components.

[0061] Based on the vertex multipath cut set and the set of uncut components, a component relationship model is established. The component relationship model includes the dependency relationship between components, the information flow of data transmitted between components, and the control flow of the execution process across components.

[0062] The vertex multi-way cut set, the uncut component set, and the component relationship model are integrated into the multi-layered context knowledge graph, and a functional module layer and a key interface layer are added to obtain an enhanced context knowledge graph.

[0063] Furthermore, based on the functional descriptions and constraints in the code skeleton and the joint vector representation, and combined with the syntax features and programming paradigms of the Scala language, the core code implementing the main functionality is generated, resulting in functional code, including:

[0064] Based on the functional module layer and key interface layer in the enhanced context knowledge graph, the functional requirements to be generated are decomposed into relatively independent sub-tasks. Each sub-task corresponds to an uncut component or cross-component function in the uncut component set, thus obtaining a set of sub-tasks.

[0065] Identify the nodes that connect different uncut components in the vertex multi-way cut set, generate interface code corresponding to the nodes, the interface code defines the interaction contract between different functional modules, and obtain the interface code set;

[0066] Based on the set of uncut components, for each uncut component in the set of uncut components, corresponding implementation code is generated according to the internal dependency structure and functional characteristics of the uncut component, resulting in a set of internal implementation code for the module.

[0067] Based on the component relationship model and the interface code set, code for inter-module collaboration is generated. The code for inter-module collaboration includes dependency injection mechanism, event passing logic, asynchronous communication implementation and error propagation handling, thus obtaining inter-module collaboration code.

[0068] The interface code set, the module internal implementation code set, and the inter-module collaboration code are assembled into a complete implementation. The type consistency and semantic consistency of cross-module references are checked to ensure type safety and semantic matching between different modules, thus obtaining the functional code.

[0069] Furthermore, after generating the core code that implements the main function based on the functional description and constraints in the code skeleton and the joint vector representation, combined with the syntax features and programming paradigm of the Scala language, the process further includes:

[0070] Based on the functional code, a code generation graph is constructed. The vertices of the code generation graph represent the code segments to be selected and the corresponding program states during the code generation process. The edges represent the transition paths of code generation, and the edge weights represent the probability or quality score of state transitions, resulting in a weighted code generation graph.

[0071] Applying the two-forest representation theory to the weighted code generation graph, the weighted code generation graph is decomposed into multiple spanning tree forests. Each two-forest contains several unconnected tree structures. The root node of each tree represents a key code decision point and each tree corresponds to a cohesive code generation logic fragment, thus obtaining a set of two-forests.

[0072] Based on the aforementioned two-forest set, a two-forest sampling algorithm based on Markov chain Monte Carlo is designed. The transition kernel function between the two forests is defined, and the Metropolis-Hastings algorithm is applied to calculate the acceptance probability of state transition. By randomly walking, N representative two forests are sampled from the code generation space to obtain the sampled two-forest set.

[0073] Deterministic point processing technique is applied to the sampled binary forest set to calculate the similarity matrix between samples in the sampled binary forest set. By maximizing the determinant of the sample set to ensure diversity, the k-DPP algorithm is applied to select the k most representative samples from the sampled binary forest set to obtain a diverse set of candidate solutions.

[0074] For each code implementation scheme in the diverse candidate scheme set, construct the Markov chain transition matrix corresponding to the code implementation scheme, calculate the stationary distribution and average first-arrival time matrix of the Markov chain transition matrix, and calculate the Kemoni constant based on the stationary distribution and the average first-arrival time matrix to obtain the Kemoni constant score.

[0075] Based on the Kemoni constant score, static analysis tools are used to analyze the time and space complexity of each code implementation scheme in the diverse candidate scheme set to obtain a code performance prediction score.

[0076] Calculate the abstract syntax tree edit distance between any two code implementation schemes in the diverse candidate scheme set to obtain the structural dissimilarity matrix, and calculate the average structural dissimilarity of each code implementation scheme with other schemes based on the structural dissimilarity matrix to obtain the diversity index score;

[0077] Based on the Kemoni constant score, the code performance prediction score, and the diversity index score, a weighted sum is performed according to a preset weighting coefficient to calculate the comprehensive quality score of each code implementation scheme in the diverse candidate scheme set;

[0078] The optimal code solution is obtained by selecting the code implementation scheme with the highest overall quality score.

[0079] Furthermore, the optimal code solution undergoes code formatting, type checking, performance optimization, and testing, and documentation comments are automatically generated to obtain the final, complete code, including:

[0080] Based on the project's unique style fingerprint, indentation style, line break rules, naming conventions, and space usage rules are extracted to construct formatting configuration parameters and obtain the project style configuration.

[0081] The optimal code scheme is formatted using the Scalafmt tool according to the project style configuration, unifying the formatting elements including indentation, line breaks, and spaces to obtain standardized code.

[0082] The Scala compiler's type checking function is invoked to perform static type analysis on the code of the specified format to verify type safety. The output is a type check report containing the location and type of type error and suggested corrections. Based on the suggested corrections in the type check report, the detected type errors or type mismatches are corrected to obtain type-safe code.

[0083] Static analysis tools were used to evaluate the performance of the type-safe code, identifying potential performance bottlenecks, including redundant traversals that repeatedly perform the same calculations within a single loop, repeated calculations that evaluate the same expression multiple times, and boxing / unboxing operations that frequently convert between value types and reference types. Performance optimization patterns were then applied to refactor the code, resulting in performance-optimized code.

[0084] Based on the input / output types and functional descriptions of the performance-optimized code, unit test cases covering normal input, boundary conditions, and abnormal situations are automatically generated, and the functional correctness of the code is verified by executing the tests, resulting in the verified code.

[0085] Analyze the structure and functionality of the verified code, automatically generate documentation comments that conform to the Scaladoc specification, including method descriptions, parameter descriptions, return value explanations, and usage examples, to obtain the final, improved code.

[0086] Furthermore, the static analysis tool is used to perform a performance evaluation on the type-safe code, identifying potential performance bottlenecks including redundant traversal that repeatedly performs the same calculation within a single loop, repeated calculations that evaluate the same expression multiple times, and boxing / unboxing operations that frequently convert between value types and reference types. Performance optimization patterns are then applied to refactor the code, resulting in performance-optimized code, including:

[0087] Based on historical versions of the project or simulated future expansion scenarios, a dynamic evolution graph of the project code is constructed. The nodes of the dynamic evolution graph of the project code represent the state of code elements at different points in time, the edges represent the dependencies between elements and their evolution, and the time dimension records the temporal information of state changes, thus obtaining a time-varying code evolution graph.

[0088] For each time slice of the time-varying code evolution graph, the corresponding Caimony constant is calculated. By analyzing the time series characteristics and changing trends of the Caimony constant, the fitness index based on the Caimony constant and its corresponding rate of change is calculated to obtain the fitness index sequence.

[0089] Based on the adaptive index sequence, the standard deviation of the adaptive index between adjacent time slices is calculated. Substructures with standard deviations exceeding a preset fluctuation threshold are identified as vulnerable points with large fluctuations in the Kemoni index. The degree centrality of each node in the time-varying code evolution graph is calculated. Nodes with the top 20% degree centrality and whose edge connection number changes in adjacent time slices exceed a preset connection stability threshold are identified as vulnerable points with high centrality but unstable connections. Time-related dependency change patterns are analyzed to identify potential vulnerable points, resulting in a set of code vulnerability points.

[0090] For the set of code vulnerabilities, a forward-looking optimization strategy is applied, which involves enhancing the abstraction layer and interface design, introducing the adapter pattern to reduce direct dependencies, applying the strategy pattern to enhance scalability, and optimizing resource management and concurrency control mechanisms to obtain forward-looking optimized code.

[0091] Based on the optimization results of the forward-looking optimized code, a code adaptability analysis report is generated, which includes potential risk points and their evolution sensitivity, applied forward-looking optimization measures, long-term maintenance recommendations, and a quantitative assessment of adaptability indicators. This code adaptability analysis report is used as supplementary content to the code documentation to obtain the performance-optimized code.

[0092] Another aspect of the present invention provides an intelligent Scala code generation system based on artificial intelligence, comprising:

[0093] The requirement parsing module is used to obtain the natural language requirement description input by the user, perform semantic understanding and information extraction on the natural language requirement description, extract functional description, input and output types and constraints, obtain a standardized set of technical elements, and map the standardized set of technical elements to the syntax structure and programming paradigm of the Scala language to obtain the requirement semantic vector.

[0094] The context-aware module is used to perform static analysis on the project codebase, extract the project code structure, dependencies and coding style, construct a multi-level context knowledge graph, and use graph neural networks and attention mechanisms to process the multi-level context knowledge graph to obtain context vector representations.

[0095] The code generation engine is used to fuse the requirement semantic vector and the context vector representation to obtain a joint vector representation, and to generate a code framework and core code based on the joint vector representation using a pre-trained Scala code generation model. Multiple candidate code implementation schemes are generated through a beam search algorithm, and the optimal scheme is selected to obtain the optimal code scheme.

[0096] The code optimization and verification module is used to perform code formatting, type checking, performance optimization, and test verification on the optimal code solution, automatically generate documentation comments, and obtain the final and improved code.

[0097] Through the above technical solution, the present invention has the following beneficial effects:

[0098] 1. Through a deep understanding of Scala's language features, high-quality code can be generated that fully utilizes Scala's syntax features and programming paradigms, thus better leveraging the advantages of the Scala language compared to general code generation tools;

[0099] 2. Through a context-aware mechanism, it can analyze the existing code structure and style of the project, generate code that is consistent with the project style, significantly reduce code integration costs, and improve the consistency of project code;

[0100] 3. Through a multi-factor code scoring mechanism, candidate code solutions can be comprehensively evaluated from three dimensions: language model probability, type matching degree, and performance score, to select the optimal solution and improve the quality of generated code;

[0101] 4. Through a comprehensive code optimization and verification process, the type safety and performance characteristics of the generated code can be ensured, runtime errors can be reduced, and the reliability and efficiency of the code can be improved.

[0102] 5. By using graph neural networks and attention mechanisms, we can gain a deeper understanding of the project's structural features and context, generate code that better fits the overall project architecture, and reduce technical debt. Attached Figure Description

[0103] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0104] Figure 1 This is a flowchart illustrating the intelligent Scala code generation method based on artificial intelligence provided in an embodiment of the present invention;

[0105] Figure 2This is a schematic diagram of the structure of the AI-based intelligent Scala code generation system provided in an embodiment of the present invention. Detailed Implementation

[0106] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0107] like Figure 1 As shown, this invention provides an intelligent Scala code generation method based on artificial intelligence, including:

[0108] Step S1: Obtain the natural language requirement description input by the user, perform semantic understanding and information extraction on the natural language requirement description, extract the functional description, input / output type and constraints, obtain a standardized set of technical elements, and map the standardized set of technical elements to the syntax structure and programming paradigm of the Scala language to obtain the requirement semantic vector.

[0109] In one embodiment of the present invention, the step of obtaining a natural language requirement description input by the user, performing semantic understanding and information extraction on the natural language requirement description, extracting functional descriptions, input / output types, and constraints to obtain a standardized set of technical elements, and mapping the standardized set of technical elements to the syntax structure and programming paradigm of the Scala language to obtain a requirement semantic vector, includes: obtaining a natural language requirement description input by the user; performing text cleaning, word segmentation, part-of-speech tagging, and named entity recognition processing on the natural language requirement description to obtain a standardized text representation; and based on the standardized text representation, performing semantic encoding through a pre-trained BERT model and identifying the user's core intent through an intent classifier. The process involves expressing an intent and simultaneously extracting the subject, behavior, and modifier relationships from the requirement through dependency parsing to obtain a structured intent representation. Based on this structured intent representation, an information extraction model is used to extract three types of technical information—functional description, input / output type, and constraints—from the structured intent representation to obtain a standardized set of technical elements. Based on the functional description and constraints in the standardized set of technical elements, the standardized set of technical elements is mapped to the specific syntax structure and programming paradigm of the Scala language to obtain a mapped Scala semantic representation. Finally, the CodeBERT code pre-training model is used to vectorize the mapped Scala semantic representation to obtain the requirement semantic vector.

[0110] Specifically, firstly, the system obtains user input in natural language, describing their requirements through the user interface or IDE plugins. These descriptions may include various forms of expression, such as "create an efficient sorting function" or "a thread-safe cache is needed to handle concurrent operations." The obtained text first enters a preprocessing flow, which includes four key steps. Text cleaning removes redundant spaces, punctuation, and special characters, and converts the text to a uniform format, such as converting all text to lowercase or using standardized abbreviations. Word segmentation divides continuous text into independent tokens. A segmenter optimized for programming domains correctly identifies and preserves programming-related proper nouns and compound terms, such as "LinkedList," "ConcurrentHashMap," or "flatMap," without incorrect segmentation. Part-of-speech tagging marks each token with its grammatical role, such as noun, verb, or adjective, which helps in understanding the function of words in sentences. Named entity recognition identifies technical entities in the text, such as data structure names, algorithm names, and design pattern names, and labels them as specific categories. The output of these four steps is a standardized text representation that retains the semantic information of the original requirements while having structured features, facilitating subsequent deep semantic analysis.

[0111] Second, based on standardized text representations, we move into the deep semantic understanding stage. A pre-trained BERT model is used for semantic encoding of the text. The BERT model, through its bidirectional Transformer architecture, can consider the complete context of words, generating context-sensitive word vector representations. The BERT model used has undergone domain-adaptive fine-tuning on technical document and code comment datasets, making it better at understanding programming-related semantics. The semantic encoding process maps text to points in a high-dimensional vector space; texts with similar semantics are mapped to nearby points. Simultaneously, a specially trained intent classifier is integrated. This classifier, built on BERT encoding, uses fully connected layers and a softmax classification head to map text to predefined programming intent categories, such as "sorting," "filtering," "aggregation," and "concurrency processing." The intent classifier is trained using supervised learning and extensive labeled programming task description data. Furthermore, dependency parsing techniques are applied to construct a syntactic dependency tree. This process employs a neural network-based parser to identify the syntactic relationships between the subject (usually representing the object of an operation, such as "list" or "string"), predicate (usually representing an operation, such as "sorting" or "converting"), and modifiers (usually representing constraints or conditions, such as "fast" or "concurrent"). These three components work together to generate a structured intent representation that captures the core semantic structure and relationships of user needs, forming the basis for subsequent extraction of technical elements.

[0112] Third, based on structured intent representation, a specially designed information extraction framework is used to extract three types of core technical information. This framework employs a multi-head attention mechanism for selective information focusing, with each attention head focusing on different types of technical elements. For extracting function descriptions, a function recognizer is used. This is a sequence labeling model based on Conditional Random Field (CRF) enhancement, capable of identifying key segments in the text that describe functions. The function recognizer focuses on verb phrases and function nouns, such as "sorted list," "cached data," and "parallel processing." This component also integrates a knowledge base from the programming domain, enabling the mapping of natural language descriptions to standardized sets of programming functions. For extracting input and output types, the system uses a type recognizer. This is a model based on Graph Convolutional Network (GCN) that operates on dependency syntax trees to identify type information related to function inputs and outputs. The type recognizer can handle complex type expressions, such as generics ("integers in a list" → "List[Int]"), composite types ("string to integer mapping" → "Map[String, Int]"), and function types ("function that receives an integer and returns a boolean value" → "Int => Boolean"). For constraint extraction, the system uses a constraint analyzer, a hybrid system that integrates rules and learning, capable of identifying performance constraints (such as time complexity, space complexity, and response time requirements), safety constraints (such as thread safety, transaction safety, and exception handling), and functional constraints (such as input validation and boundary condition handling). The constraint analyzer uses a semantic matching algorithm to map natural language constraint expressions to formal constraint representations. Finally, the outputs of these three components are merged into a unified set of technical elements by a structured integrator. This set is represented by a graph structure, where nodes represent individual technical elements and edges represent dependencies and relationships between them.

[0113] Fourth, based on a standardized set of technical elements, Scala-specific mapping transformations are performed. This stage employs a two-level mapping architecture. First, the function mapping engine maps abstract functional descriptions to specific Scala syntax structures. The function mapping engine contains a rich library of mapping rules, covering the correspondence between common programming functions and their Scala implementations. For example, the "sorting" function is mapped to the `sorted`, `sortBy`, or `sortWith` methods, selecting the most suitable variant based on context and constraints; the "asynchronous processing" function is mapped to the `Future` or `Actor` model; and the "data transformation" function is mapped to the `map`, `flatMap`, or `collect` operations. The mapping process considers various Scala expressions, selecting the implementation best suited to the current context. Second, the paradigm mapping engine maps constraints to Scala's programming paradigms. Scala supports multiple programming paradigms, including functional programming, object-oriented programming, and concurrent programming. The paradigm mapping engine analyzes the constraints to determine the most suitable programming paradigm. For example, "thread safety" constraints might map to immutable data structures (such as immutable collections) or concurrency control mechanisms (such as synchronized blocks or atomic variables); "high performance" constraints might map to lazy computation (lazy val), tail recursion optimization (@tailrec), or parallel collections (par). The paradigm mapping process uses a decision tree algorithm to select the optimal programming paradigm based on the combination and priority of multiple constraints. Finally, the semantic integrator integrates the results of the function mapping and paradigm mapping to generate a complete Scala semantic representation. This representation contains all the Scala language elements needed to implement user requirements, including class / object definitions, method signatures, type declarations, function body structures, and programming mode selection.

[0114] Fifth, a cross-modal feature extraction mechanism involving serial collaboration between the BERT and CodeBERT models is employed to vector-encode the integrated Scala semantic representation. Since user-input natural language requests typically contain a large amount of non-code-related business logic, colloquial expressions, and complex modifiers, directly using the bimodal CodeBERT model to process such long texts can easily introduce noise and lead to intent recognition bias. Therefore, in this embodiment, the BERT model, which performs better on pure natural language understanding tasks, is first used for preliminary semantic encoding and dependency parsing to accurately extract structured intent representations (including subject, behavior, and modifier relationships). Subsequently, these structured intents are mapped to semantic representations conforming to the characteristics of the Scala language. Then, the CodeBERT model is used to vector-encode this "mapped Scala semantic representation." This collaborative mechanism leverages both BERT's advantages in natural language syntactic analysis and CodeBERT's expertise in programming language semantic representation, effectively avoiding semantic drift issues when a single model handles complex natural language requests. In the specific implementation, the mapped Scala semantic representation is converted into an input format acceptable to CodeBERT, including tokenization, special tag addition, and input truncation / padding. The converted input is passed through the encoder layer of the CodeBERT model to generate a context-sensitive vector representation. The [CLS] tag vector from the last hidden state is extracted as the vector encoding for the entire semantic representation; this is a dense vector with a dimension of 768. To enhance the quality of the vector representation, vector optimization techniques are also applied. First, a Domain Adaptation Layer is used, a linear layer fine-tuned on a Scala-specific codebase, mapping the general CodeBERT vectors to a vector space more suitable for Scala characteristics. Second, feature enhancement techniques are applied, fusing explicit features directly extracted from the set of technical elements. These features are represented in the form of one-hot encoding or embedded vectors, including function categories, type information, and constraint categories. Finally, vector calibration techniques are used to ensure that the distance between vectors generated for similar needs in the vector space reflects their semantic similarity. After these optimization processes, the final requirement semantic vector is generated. This vector accurately captures the semantic content of the user's requirements, the Scala language-specific implementation, and the programming paradigm selection, providing high-quality semantic guidance for the subsequent code generation process.

[0115] Sixth, to further enhance the adaptability of requirement semantic vectors to the Scala domain, Domain-Adaptive Vector Representation (DAVR) technology was implemented. DAVR is a vector representation method optimized for specific programming languages ​​and domains. It incorporates Scala-specific syntax structures and programming paradigm features on top of general code representations. Specifically, the implementation first constructs a Scala domain knowledge enhancement layer, a structured knowledge base containing Scala language features, standard library functions, and common programming patterns. This layer interacts with CodeBERT-encoded vectors through a self-attention mechanism, highlighting Scala-related features and suppressing general but Scala-irrelevant features. Secondly, programming paradigm-sensitive encoding is applied, an encoding method capable of distinguishing features from different programming paradigms. Through a multi-task learning framework, it simultaneously predicts the functional category and programming paradigm category of code snippets, enabling the generated vectors to better capture information from both dimensions. Finally, using contrastive learning, positive sample pairs (different expressions of the same function) and negative sample pairs (expressions of different functions) are constructed. The vectors generated by the trained model form a clear clustering structure in the vector space, mapping similar functions to nearby vectors and different functions to distant vectors. This combination of three techniques ensures that the final generated requirement semantic vector not only contains general code semantics but also specifically enhances Scala-specific language features and programming paradigm information, providing more accurate semantic guidance for subsequent Scala code generation.

[0116] In one embodiment of the present invention, the step of extracting three types of technical information—functional description, input / output type, and constraint conditions—from the structured intent representation using an information extraction model to obtain a standardized set of technical elements includes: identifying keywords describing functions using a specially trained information extraction model based on the structured intent representation, extracting functional descriptions of ranking algorithms or data filtering to obtain a set of functional descriptions; identifying type-related semantic components using the information extraction model based on the structured intent representation, extracting the correspondence between input types and output types to obtain a set of input / output types; identifying performance and security constraint-related semantic components using the information extraction model based on the structured intent representation, extracting thread safety requirements and time complexity limits to obtain a set of constraint conditions; and structurally integrating the set of functional descriptions, the set of input / output types, and the set of constraint conditions to obtain the standardized set of technical elements.

[0117] Specifically, firstly, after obtaining the structured intent representation, a deep-level analysis of the intent is performed. The structured intent representation is a graph structure where nodes represent core concepts and entities in the requirement, and edges represent semantic relationships between these concepts. A graph traversal algorithm is used to systematically analyze this structure, identifying action nodes representing operational behaviors (such as "sorting," "filtering," and "converting"), entity nodes representing data objects (such as "list," "set," and "string"), and modifier nodes representing constraints (such as "efficient," "thread-safe," and "parallel"). To process this structured data, three specialized information extraction models are initialized: a function identification model, a type inference model, and a constraint analysis model. These three models share a basic neural network architecture but are specifically optimized for their respective extraction tasks. The basic architecture uses a Graph Attention Network (GAT), which can capture dependencies and contextual information between nodes. The system also loads a domain-specific knowledge base, including a programming concept ontology, a Scala type system mapping table, and a performance / safety constraint classification system. These knowledge resources provide fundamental support for subsequent accurate extraction.

[0118] Second, based on the structured intent representation, the function descriptions are first extracted and normalized. The function recognition model adopts a multi-level feature extraction architecture. First, semantic similarity calculation is used to match the action nodes in the intent representation with a predefined function category vocabulary. This vocabulary contains hundreds of common programming operations, such as "sorting," "filtering," and "mapping," and each operation also contains a set of synonyms. The matching process uses a soft matching algorithm, which can still identify the actual intent even if the user uses non-standard expressions (such as "arrange in order" instead of directly using "sort"). For each matched function, its operation parameters and modifier information are further extracted. For example, for "quickly sort a list of integers," "quickly" is identified as a modifier of the sorting algorithm, and "list of integers" is the operation object. The function recognition model also uses context-sensitive disambiguation technology to handle the problem of terminology ambiguity. For example, "filtering" may refer to data screening or signal processing in different contexts; the actual semantics are determined by analyzing relevant nodes. Finally, the identified function descriptions are standardized, mapping diverse natural language expressions to unified function identifiers. This process uses a functional semantic normalizer to convert various expressions (such as "sort", "sort", "arrange in order") into a standard functional description (such as "sort") while preserving their parameters and modifiers. The normalized set of functional descriptions contains all the core functions that the system needs to implement. Each functional item contains a functional identifier, operation parameters, modifiers, and dependencies between functions.

[0119] Third, the type inference model is used to extract input and output type information from the structured intent representation. Type inference is a two-stage process: explicit type extraction and implicit type inference. In the explicit type extraction stage, the system identifies type information directly mentioned in the intent representation, such as "list of integers," "string-to-integer mapping," etc. This process uses a type terminator to identify phrases representing types through pattern matching and semantic analysis. The type terminator contains a dictionary of type expressions that maps natural language type descriptions to the Scala type system. For example, "list of integers" maps to "List[Int]," and "string-to-integer mapping" maps to "Map[String, Int]." In the implicit type inference stage, cases where the type is not explicitly mentioned are handled. The implicit type inferrer infers possible types based on contextual information and functional semantics. For example, if a user mentions "sorting values" but does not specify a type, the input is inferred to be some kind of set type based on the knowledge that "sorting" operations are usually applied to set types. Type propagation is also considered for data flow relationships between functions. If the output of function A is the input of function B, then the input type of B must be compatible with the output type of A. The type constraint solver is responsible for resolving this type propagation problem, ensuring type consistency throughout the system. Ultimately, the system generates a complete set of input and output types, containing the input type, output type, and type parameter information for each functional component. This set uses Scala's type system notation, providing strict type guidance for subsequent code generation.

[0120] Fourth, a constraint analysis model is used to extract performance and security constraints from the structured intent representation. Constraint extraction is a multi-dimensional identification process covering various constraint categories. First, performance-related constraint expressions are identified, including time complexity constraints (e.g., "O(n log n)", "linear time"), space complexity constraints (e.g., "memory efficient", "low space overhead"), and response time constraints (e.g., "real-time response", "low latency"). The performance constraint identifyr uses a hybrid approach, combining rule matching and deep learning to capture various forms of performance expressions. For formal complexity representations (e.g., Big O notation), a dedicated parser is used; for informal expressions (e.g., "efficient", "fast"), semantic matching techniques are used to map them to specific performance levels. Security-related constraints are also identified, including thread safety (e.g., "thread safe", "concurrency safe"), data security (e.g., "encryption", "secure transmission"), and exception handling requirements (e.g., "error recovery", "fault tolerance"). The security constraint analyzer determines the scope and stringency of security requirements through contextual analysis, distinguishing between different levels of requirements such as "must be thread-safe" and "consider thread safety." Furthermore, it identifies functional constraints (such as "handling null values" and "validating input") and resource constraints (such as "low memory usage" and "reducing network calls"). The constraint conflict detector analyzes potential conflicts between different constraints, such as trade-offs between time and space efficiency, and determines constraint priority based on context. Finally, it generates a complete set of constraints, each containing constraint type, constraint level (mandatory, should, optional), constraint scope (global or specific function), and constraint parameters.

[0121] Fifth, after independently extracting functional descriptions, type information, and constraints, a comprehensive integration of technical elements is performed to construct a structured set of technical elements. This integration process employs a multi-level relational modeling approach. First, a functional dependency analyzer is used to construct a dependency graph between functions, identifying call relationships, data flow relationships, and control flow relationships between functions. Dependency analysis considers the semantics and context of functions; for example, "sorting filtered data" indicates that the sorting function depends on the filtering function. Second, a type consistency checker is used to verify the consistency of type information throughout the functional chain, ensuring that the output type of each function meets the input type requirements of the next function. Type consistency checking also considers Scala's type conversion and implicit conversion mechanisms, allowing compatible but not fully matching type connections. Third, a constraint propagation engine is used to analyze how constraints affect the various components in the functional chain. For example, a global "thread-safe" constraint needs to propagate to all functional components, while a performance constraint for a specific function may only affect a portion of the implementation. Constraint propagation also considers the combined effects of constraints, such as how multiple local performance constraints affect overall performance. Finally, a technical element integrator is used to merge these analysis results into a unified, hierarchical set of technical elements. This set is represented using a graph data structure, where nodes include functional descriptions, type information, and constraints, and edges represent dependencies, influence relationships, and constraints between them. This structured representation captures the complete technical essence of the user's requirements, providing a comprehensive information foundation for subsequent Scala language-specific mappings.

[0122] Sixth, to improve the accuracy and robustness of technical element extraction, an Adaptive Extraction Strategy Framework (AESF) was implemented. AESF can dynamically adjust the extraction strategy based on the characteristics of the input requirements, adapting to requirement descriptions of varying complexity and expression. First, AESF includes a requirement complexity estimator, which determines the complexity of the requirement by analyzing the number of nodes, relation density, and semantic depth of the structured intent representation. For simple requirements (such as single functions or explicit types), the system employs a direct extraction strategy; for complex requirements (such as multi-step operations or implicit types), an iterative refinement strategy is used. Second, AESF implements a multi-source evidence integration mechanism, comprehensively considering multiple information sources to improve extraction accuracy. For example, in type inference, the system considers not only direct type representations but also functional semantics (certain functions are often associated with specific types), project context (commonly used data types in the project), and domain knowledge (commonly used data structures in a specific domain). This multi-source evidence approach can make more accurate inferences even with incomplete information. Third, AESF includes extraction uncertainty management, explicitly tracking the confidence level of each extraction result. A confidence score is assigned to each extracted functional description, type information, and constraint condition to indicate the reliability of the extraction. For extraction results with low confidence, multiple candidate options may be generated or alternative implementations may be provided in subsequent code generation. Finally, AESF implements an extraction error recovery mechanism capable of detecting and correcting potential errors in the extraction process. For example, if a type inconsistency is detected (such as a mismatch between function output and the next function input), a type re-inference process is triggered to attempt to resolve the inconsistency. These mechanisms collectively improve the robustness and adaptability of technical element extraction, enabling the system to handle requirement descriptions of various forms and qualities.

[0123] Seventh, to deepen the semantic understanding of programming requirements, a Domain Knowledge Enhancement Mechanism (DKEM) was implemented. DKEM improves the accuracy and comprehensiveness of technical element extraction by incorporating professional knowledge from the programming domain. A programming concept ontology was constructed, a structured knowledge base containing concepts, relationships, and constraints within the programming domain. This ontology covers common data structures (such as lists, trees, and graphs), algorithms (such as sorting, searching, and optimization), design patterns (such as singletons, observers, and factories), and programming paradigms (such as functional, object-oriented, and reactive programming). Semantic extension reasoning was also implemented, automatically expanding implicit information in user requirements based on ontology knowledge. For example, when a user mentions "implementing binary search," it can be inferred that this requires an ordered data structure and may involve logarithmic time complexity. Furthermore, a programming idiom recognizer is used to identify programming idioms and common patterns in user requirements. For example, it identifies the "producer-consumer" pattern, the "read-write lock" mechanism, or the "lazy loading" strategy. These programming idioms typically have standard implementations that can be directly mapped to corresponding code patterns. DKEM also includes a Scala-specific language feature library, covering Scala-specific language concepts such as traits, implicit conversions, type classes, and pattern matching. This Scala-specific knowledge helps to more accurately interpret user requirements and select the most suitable Scala implementation in the subsequent mapping stage. By integrating domain knowledge, it enables the understanding of high-level programming concepts and intentions, going beyond superficial syntax analysis, thereby improving the quality of technical element extraction.

[0124] In one embodiment of the present invention, based on the functional descriptions and constraints in the standardized set of technical elements, the standardized set of technical elements is mapped to specific syntax structures and programming paradigms of the Scala language to obtain a mapped Scala semantic representation; the mapped Scala semantic representation is vector-encoded using the CodeBERT code pre-training model to obtain the requirement semantic vector, including: based on the functional descriptions in the standardized set of technical elements, identifying the corresponding specific syntax structures of the Scala language, mapping sorting intents to Scala set operation methods, mapping asynchronous processing intents to Future or Actor models, and mapping data transformation intents to... The syntax structure mapping result is obtained by performing a map or flatMap operation; based on the constraints in the standardized set of technical elements, the corresponding Scala programming paradigm is identified, and thread safety requirements are mapped to immutable data structures or synchronization mechanisms, and performance requirements are mapped to tail recursion optimization or parallel sets, resulting in a programming paradigm mapping result; the syntax structure mapping result and the programming paradigm mapping result are integrated to form a complete Scala semantic representation, resulting in an integrated Scala semantic representation; using the CodeBERT code pre-trained model, feature extraction and vector encoding are performed on the integrated Scala semantic representation to generate a 768-dimensional semantic vector, resulting in the required semantic vector.

[0125] Specifically, based on a standardized set of technical elements, functional descriptions and constraints are mapped to specific structures and programming paradigms of the Scala language, and then semantic vector representations are generated using an advanced code pre-training model. This process ensures a precise correspondence between requirements and code implementation, laying a solid foundation for subsequent code generation.

[0126] First, based on the functional descriptions within a standardized set of technical elements, the system identifies and maps them to corresponding Scala language-specific syntactic structures. A semantic intent analyzer is implemented, which parses key verbs and objects in the functional descriptions to extract the core operational intent. The system uses a Scala syntactic mapping knowledge base, a professional knowledge base containing mappings from common functional intents to Scala syntactic structures, covering best practices for hundreds of common programming tasks. Natural language understanding techniques are applied to the functional descriptions to identify key functional intent categories, such as sorting intents, filtering intents, transformation intents, or asynchronous processing intents.

[0127] For identified sorting intentions, they are mapped to Scala collection operation methods such as `.sorted`, `.sortBy`, or `.sortWith`, selecting the most suitable method based on sorting complexity and custom requirements. The stability requirements and performance impact of sorting are also considered; `.sortBy` is used when stable sorting is required, while `.sortWith` is considered in conjunction with a custom comparator in performance-critical scenarios. For asynchronous processing intentions, the complexity of the concurrency model is analyzed. Simple asynchronous computations are mapped to Scala's `Future`, such as parallel API calls or I / O operations; while complex stateful concurrent logic is mapped to the Actor model, using the Akka framework to implement a complex concurrent system containing multiple interacting entities.

[0128] For data transformation intents, a refined mapping is performed based on the characteristics of the transformation type. One-to-one transformations are mapped to `.map` operations, one-to-many or filtering transformations are mapped to combinations of `.flatMap` and `.filter`, and aggregation operations are mapped to `.fold` or `.reduce`. Special attention is paid to chained transformation scenarios, employing transformation optimization strategies to reorganize multi-step transformations into efficient combined operations, such as using `.collect` instead of `.filter` followed by `.map`. The system also considers data volume and performance requirements, favoring lazy evaluation transformation chains when processing large datasets, such as using `.view` or the Stream API. Through these refined mappings, abstract functional intents are translated into concrete Scala language syntax structures, forming preliminary syntax structure mapping results.

[0129] Next, based on the constraints within the standardized set of technical elements, the corresponding Scala programming paradigm is identified. A constraint resolver is implemented, which analyzes various constraints in the functional requirements, such as concurrency safety, performance requirements, memory usage limits, and error handling strategies. A Scala paradigm selection matrix is ​​then used—a multi-dimensional decision matrix—to recommend the most suitable Scala programming paradigm and design pattern based on different combinations of constraints.

[0130] For thread safety requirements, analyze the patterns and frequency of concurrent access, mapping high-concurrency read scenarios to immutable data structures to ensure safe data access in concurrent environments. Map scenarios requiring concurrent modification to appropriate synchronization mechanisms, such as synchronized blocks, atomic references, or concurrent collections. Pay special attention to Scala-specific concurrency control mechanisms, such as message passing isolation in the Actor model and Software Transactional Memory (STM), applying these advanced models in complex concurrency scenarios.

[0131] For performance requirements, assess the ratio of computationally intensive to I / O-intensive operations, map recursive algorithms to tail-recursive optimization patterns, and leverage the tail-recursive optimization features of the Scala compiler to eliminate stack overflow risks and improve performance. Map data parallel processing requirements to parallel collections or Fork / Join frameworks to fully utilize multi-core processing capabilities. Consider memory efficiency constraints, choose appropriate data structures and processing strategies, such as using Iterators instead of complete collections, or applying lazy computation patterns to postpone computation until the result is actually needed. Through these mappings, constraints are translated into specific choices of Scala programming paradigms, forming the programming paradigm mapping result.

[0132] The system integrates the syntactic structure mapping results and the programming paradigm mapping results to form a complete Scala semantic representation. A semantic integration engine is implemented, which checks the consistency and compatibility of the two types of mapping results and resolves potential conflicts. A priority reconciliation strategy is implemented, determining trade-offs based on the importance of requirements and technical feasibility when syntactic structures and programming paradigms conflict. For example, when high-performance requirements conflict with strict immutability, the system may choose a compromise, such as using an immutable exterior but internally optimized data structure.

[0133] Semantic enhancements are performed, adding implicit elements not explicitly stated during the mapping process but required by Scala best practices, such as type parameters, implicit conversions, or context bindings. Project-specific coding conventions are also considered, adjusting the generated semantic representation to conform to the project style. Consistency verification ensures that the integrated semantic representation is logically self-consistent, that interfaces between components match, and that the type system is consistent. The integration process forms a complete Scala semantic representation, encompassing all the syntactic structures, programming paradigms, and design pattern choices required for functionality implementation, serving as the foundation for subsequent vector coding.

[0134] Finally, the CodeBERT pre-trained model is used to extract features and encode vectors into the integrated Scala semantic representation. CodeBERT is a pre-trained model designed specifically for program code. Based on the Transformer architecture, it learns the syntactic structure and semantic patterns of code through training on a large code corpus. The system implements Scala semantic tokenization, converting the integrated Scala semantic representation into a token sequence that CodeBERT can process, handling Scala-specific syntactic elements such as type parameters, infix operators, and for-comprehensions.

[0135] Context-aware encoding enhancements are applied to the labeled sequences, adding special tags to identify different types of semantic elements, such as class definitions, method signatures, or expression statements, helping the model to more accurately understand the code structure. Long sequence processing is performed to handle complex semantic representations that may exceed the model's input length limits, using sliding window or hierarchical encoding strategies to ensure complete capture of semantic information. Deep features are extracted using CodeBERT's encoder component, generating a 768-dimensional semantic vector. This vector accurately expresses the functional intent and constraints of the requirement in high-dimensional space, encoding the key information needed for the Scala implementation.

[0136] Semantic vector post-processing was also implemented, normalizing and weighting the generated vectors according to dimensional importance to ensure that key semantic features were fully expressed in the vectors. Vector quality assessment was used to verify the discriminative and expressive power of the generated vectors by comparing them with vectors of similar requirements. The final result was a high-quality requirement semantic vector that accurately captured the implementation of the original requirement in Scala, providing crucial semantic guidance for subsequent code generation.

[0137] Through this series of meticulous processes, a precise mapping from natural language requirements to Scala-specific semantic representations is achieved, and this representation is encoded into a computer-processable vector form using an advanced pre-trained model. This approach not only considers the unique characteristics of the Scala language but also incorporates best practices from modern software engineering, ensuring that the generated semantic vectors both reflect the essence of the original requirements and conform to Scala's implementation specifications, laying a solid foundation for high-quality code generation.

[0138] Step S2: Perform static analysis on the project codebase to extract the project code structure, dependencies and coding style, construct a multi-level context knowledge graph, and use graph neural networks and attention mechanisms to process the multi-level context knowledge graph to obtain context vector representations.

[0139] In one embodiment of the present invention, the step of performing static analysis on the project codebase to extract the project code structure, dependencies, and coding style, constructing a multi-level context knowledge graph, and processing the multi-level context knowledge graph using graph neural networks and attention mechanisms to obtain a context vector representation includes: constructing an abstract syntax tree for the project codebase using static code analysis tools, extracting classes, traits, objects, and methods in the project and their corresponding relationships to obtain a structured representation of the project code; parsing the project's build configuration file, extracting the external libraries that the project depends on and their corresponding version information, and analyzing the usage patterns of the external libraries in the project to obtain a project dependency graph; analyzing the existing code of the project, extracting coding style features and common programming patterns to obtain a project-specific style fingerprint; structurally integrating the structured representation of the project code, the project dependency graph, and the project-specific style fingerprint to construct a multi-level context knowledge graph containing a code structure layer, a dependency layer, and a style pattern layer; using a graph neural network to extract node features and perform message passing on the multi-level context knowledge graph, and calculating the relevance of each level of information to the current task and performing weighted fusion through an attention mechanism to obtain the context vector representation.

[0140] Specifically, a comprehensive static analysis is performed on the project's codebase to construct and process a multi-layered contextual knowledge graph, generating vector representations rich in project-specific information. This process ensures that the generated code can seamlessly integrate into the existing project ecosystem by gaining a deep understanding of the project's structure, dependencies, and coding style.

[0141] First, static code analysis tools are used to construct an abstract syntax tree (AST) for the project's codebase. An enhanced AST parser is implemented, which not only constructs the standard AST but also handles Scala-specific syntax structures. Type definitions, including classes, traits, objects, and enumerations, are parsed, extracting their names, parameters, inheritance relationships, and type parameter information. Method definitions are also analyzed, including method signatures, parameter lists, return types, modifiers, and generic information. Special attention is paid to Scala-specific syntax elements such as implicit definitions, type members, self-type annotations, and path-dependent types.

[0142] Implement symbol table construction, creating a symbol table entry for each identifier in the project, recording its declaration location, type information, and reference relationships. Perform name resolution and type inference to determine the specific definition referenced by each identifier and infer the static type of expressions, handling Scala's complex type system, including type inference, implicit conversions, and type constraints. Through analysis, track the usage locations and methods of each definition, identifying common API usage patterns and potential areas of focus for responsibility.

[0143] A hierarchical graph is constructed to represent inheritance, implementation, and mixin relationships between types, capturing Scala's rich type composition patterns. A call graph is also created to represent method call relationships, including static calls, dynamic dispatch, and higher-order function passes. These analyses collectively form a structured representation of the project code, containing a complete view of code elements and their interrelationships.

[0144] Next, the project's build configuration file is parsed to extract project dependency information. A build system parser is implemented, supporting various Scala build tools, including SBT, Maven, and Gradle, extracting dependency declarations from their respective configuration files. Direct and transitive dependencies are analyzed, recording the organization, name, version, and scope information of each external library. Dependency configurations for different environments (such as development, testing, and production) are also identified, constructing a complete dependency tree.

[0145] Dependency analysis scans the project's source code to identify actual references to external libraries, pinpointing the location and manner of each library's use within the project. API call extraction is implemented to analyze the frequency and patterns of API calls to each external library, revealing the project's dependence on and usage preferences for these libraries. Special attention is paid to implicit dependencies—dependencies not directly declared in the build files but used by the project through transitive dependencies or runtime loading.

[0146] A dependency graph is constructed, where nodes represent projects and external libraries, edges represent dependencies, and edge attributes record dependency type, version constraints, and usage intensity. Version conflict detection is implemented to identify dependency conflicts between different versions of the same library and record resolution strategies. An API compatibility view is also built to track version changes and obsolescence status of external APIs and assess the risks and impacts of dependency updates. These analyses form a complete project dependency graph, providing a comprehensive view of the project's external integration points.

[0147] Further analysis of the project's existing code reveals coding style features and common programming patterns. A style feature extractor is implemented, analyzing code formatting characteristics such as indentation style (spaces vs. tabs, indentation width), line break patterns (brake placement, method chain line breaks), and space usage (around operators, after commas). Naming conventions are also analyzed to identify the naming styles used for different elements (classes, methods, variables), such as camelCase, underscore-separated, or Hungarian notation.

[0148] Perform programming paradigm analysis to assess the project's balance between object-oriented, functional, and imperative programming paradigms. Identify the project's preferred design patterns, including classic object-oriented design patterns (such as factory, observer, decorator) and functional patterns (such as monads, application functors, function composition). The system also analyzes error handling strategies, such as the project's preference for exceptions, Option / Maybe, Try / Either, or custom error types.

[0149] We constructed a coding style model and summarized consistent coding practices for the project, such as comment style, documentation generation conventions, accessibility control patterns, and testing conventions. We paid particular attention to Scala-specific programming habits, such as implicit parameter usage patterns, type class implementation styles, and a preference for functional data structures. We also identified project-specific idiomatic patterns—recurring code structures that may reflect project-specific best practices or architectural decisions. These analyses collectively form a project-specific style fingerprint, capturing the project's coding culture and technical values.

[0150] The project's structured code representation, dependency graph, and project-specific style fingerprints are structurally integrated to construct a multi-layered contextual knowledge graph. This contextual knowledge graph is a multi-layered heterogeneous graph structure that integrates various contextual information from the project, providing a comprehensive reference for code generation. A graph fusion engine is implemented, which unifies information from the three sources into a coherent graph structure.

[0151] Construct a code structure layer, representing code elements and their relationships. Nodes are types and methods, and edges represent inheritance, implementation, and invocation relationships. Create a dependency layer, representing the project's interaction with external libraries. Nodes are project modules and external libraries, and edges represent dependencies and API calls. Also establish a style and pattern layer, representing coding standards and common patterns. Nodes are style features and design pattern instances, and edges represent application relationships and consistency constraints.

[0152] Cross-layer connection construction is implemented, creating connection edges between different layers, such as code elements' use of specific external APIs or coding style variations of specific modules. Graph validation is performed to check the integrity and consistency of the knowledge graph, repairing potential disconnections or conflicts. Graph simplification is also performed, removing redundant information that is not important for code generation, maintaining the information density and usability of the graph. This process forms a complete multi-layered contextual knowledge graph, providing rich structured information for subsequent analysis and vectorization.

[0153] Graph Neural Networks (GNNs) are used for node feature extraction and message passing in multi-layered contextual knowledge graphs. A heterogeneous GNN model is implemented, capable of handling complex graph structures with different types of nodes and edges. Dedicated feature extractors and transformation functions are defined for each type of node and edge to ensure the correct encoding and processing of different types of information. Multi-turn message passing is implemented to allow information to propagate through the graph, enabling each node to aggregate information from its neighborhood to form a context-aware representation.

[0154] We apply hierarchical aware aggregation functions, designing specialized information aggregation strategies for each level based on its semantic characteristics. We implement attention-guided message passing, dynamically adjusting the importance of information from different neighbors using an attention mechanism to enhance the focus of the representation. Furthermore, we apply graph structure self-learning, allowing the model to adaptively adjust the connection strength of the graph during message passing, highlighting important structural patterns.

[0155] The system calculates the relevance of information at each level to the current task using an attention mechanism and performs weighted fusion. A hierarchical attention module is implemented, which calculates the relevance scores between the current code generation task and each level of the knowledge graph. The system uses a multi-head attention mechanism to calculate attention from multiple representation subspaces, capturing different types of relevance patterns. Task-conditional attention is implemented, dynamically adjusting the level of attention to different levels based on the nature of the current generation task; for example, functional implementation tasks focus more on the code structure layer, while stylization tasks focus more on the style pattern layer.

[0156] Weighted hierarchical fusion is performed, fusing node representations from each level into a unified context vector based on calculated attention weights. Residual connection enhancement is implemented to preserve original hierarchical information during fusion, avoiding information loss caused by deep fusion. Adaptive feature recalibration is also applied, normalizing features based on their statistical properties to ensure balanced fusion of information from different sources.

[0157] Finally, vector post-processing is performed to denoise and enhance the fused vectors, improving representation quality. Semantic consistency calibration is implemented to ensure that the position of the vector representation in the semantic space is consistent with the technical characteristics of the project. Context vector compression is achieved to reduce vector dimensionality while retaining key information, improving the efficiency of subsequent processing. The final result is a high-quality context vector representation that encodes the project's structural features, dependencies, and coding style, providing rich contextual guidance for code generation.

[0158] This complex analysis and vectorization process enables a deep understanding of the project's technological ecosystem and development culture. The generated context vector representations not only contain the surface structure of the code but also encode deep design philosophies and architectural decisions. This comprehensive contextual understanding generates code that is both functionally correct and stylistically consistent, seamlessly integrating into the project and significantly improving the usability and acceptability of the generated code.

[0159] In one embodiment of the present invention, the step of using a graph neural network to extract node features and perform message passing on the multi-layered context knowledge graph, and calculating the relevance of each layer of information to the current task through an attention mechanism and performing weighted fusion to obtain the context vector representation includes: constructing an input representation of the graph neural network based on the multi-layered context knowledge graph, encoding nodes in the multi-layered context knowledge graph as feature vectors, and encoding edges as adjacency matrices to obtain a graph-structured input; using the graph neural network to perform multi-layer message passing and feature aggregation on the graph-structured input, updating the representation of each node by aggregating the features of neighboring nodes to obtain a node update representation; based on the node update representation, using an attention mechanism to calculate the attention weights of each layer of information in the code structure layer, dependency layer, and style pattern layer with the current task to obtain a hierarchical weight distribution; and according to the hierarchical weight distribution, performing a weighted summation of the node representations of each layer of information to highlight context features related to the current task to obtain the context vector representation.

[0160] Specifically, graph neural network technology is used to process multi-layered contextual knowledge graphs, and information from each layer is integrated through an attention mechanism to generate high-quality contextual vector representations. This process fully leverages the correlations within graph-structured data and the differences in hierarchical information, ensuring that the contextual representation accurately captures project features and is highly relevant to the current task.

[0161] First, an input representation for a graph neural network is constructed based on a multi-layered contextual knowledge graph. A heterogeneous node featurer is implemented, which generates initial feature vectors for different types of graph nodes. For nodes in the code structure layer, various semantic and structural features are extracted: type nodes contain modifier information (e.g., public, private), type categories (class, trait, object), number of members, and inheritance depth; method nodes contain parameter type lists, return types, complexity metrics, and annotation information; expression nodes contain operation types, involved variables, and constant values. Context-sensitive encoding is applied to these raw features to transform discrete features into dense vector representations, preserving the semantic relationships between features.

[0162] For nodes in the dependency layer, the system extracts metadata from dependent libraries (such as organization name, library name, and version number), API usage frequency, and calling patterns. Semantic version encoding is implemented, converting version numbers into continuous vectors so that semantically similar versions (such as patch version differences) are also similar in the vector space. For nodes in the style and pattern layer, the system extracts the type, scope, and priority of style rules; and the category, participating components, and usage frequency of design patterns. Dimensional unification is applied to all node features, mapping features from different sources to a vector space of the same dimension using a projection matrix to ensure consistency in subsequent processing.

[0163] Implement a type-aware edge encoder to create adjacency matrices for different types of relationships. It not only constructs basic binary adjacency matrices (representing the existence of connections between nodes), but also creates weighted adjacency matrices (representing connection strength) and type-based adjacency matrices (representing connection types). Special attention is paid to cross-layer connections that bridge nodes at different levels, such as connections between code elements and external APIs used, or connections between code modules and application style rules. Edge attribute embedding is applied to encode edge attributes (such as call frequency and dependency strength) into the edge representation, enriching the graph's expressive power.

[0164] A hierarchical-aware subgraph extractor is constructed to extract the most relevant subgraphs from the complete graph based on the context of the current task, reducing computational complexity and increasing information density. Task relevance scoring is used to calculate the relevance score of each node to the current task, and task-specific subgraphs are constructed based on seed nodes with high relevance. These processes collectively generate a graph-structured input containing node feature vectors and various adjacency matrices, providing rich starting information for graph neural network processing.

[0165] Next, a graph neural network is used to perform multi-layer message passing and feature aggregation on the graph-structured input. A heterogeneous graph message passing network is implemented, capable of handling complex graph structures containing multiple node and edge types. Message passing is the core mechanism of graph neural networks, allowing nodes to exchange information with their neighbors through edges, gradually forming node representations containing local structural knowledge. A specialized type-conditional message function is designed to customize message transformation logic based on the types of the sending and receiving nodes, ensuring semantic consistency in information exchange between different types of nodes.

[0166] Multi-hop knowledge fusion is implemented by using a multi-layer graph neural network structure to allow information to propagate across the graph in multiple hops, enabling nodes to acquire broader contextual knowledge. Skip connections are implemented at each layer to preserve information from earlier layers and prevent information attenuation during message passing. A graph attention layer is applied, enabling nodes to distinguish the importance of different neighbors and dynamically adjust the amount of information received from each neighbor. Special attention is paid to intra-layer message aggregation to ensure that nodes within the same layer fully exchange information and form a consistent representation within the layer.

[0167] Inter-layer message bridging is implemented through a dedicated cross-layer message passing mechanism to facilitate information exchange between different layers, such as the impact of code structure information on dependency selection or the constraints of style guidelines on code organization. Nonlinear transformation units are used to apply activation functions after message passing, enhancing the model's ability to represent complex patterns. The system also implements batch normalization to stabilize the message passing process, accelerate convergence, and improve representation quality. Through this series of carefully designed message passing and feature aggregation steps, node update representations are generated, which not only include the node's own features but also incorporate contextual information from the graph structure.

[0168] Based on node update representations, an attention mechanism is used to calculate the relevance of information at each level to the current task. A hierarchical awareness attention network is implemented, specifically designed to calculate the relevance of node representations at different levels to the current code generation task. First, a task representation vector is constructed, encoding the key features of the current task, including functional requirements, technical constraints, and priorities. Multi-head cross-attention is used to calculate the relevance between the task vector and node representations at each level from multiple representation subspaces, capturing correlation patterns from different aspects.

[0169] Multi-head attention is a mechanism that allows a model to focus on information from different locations simultaneously. By using parallel attention heads, the model can capture various types of relevance patterns. For each attention head, a query vector (based on task representation), a key vector, and a value vector (based on node representation) are defined. An attention score is calculated using query-key similarity, and these scores are used to weight and summarize the value vector. Finally, the outputs of multiple heads are merged to form the final representation.

[0170] Attention weights are calculated for each of the three main layers (code structure layer, dependency layer, and style / pattern layer). For the code structure layer, attention is paid to the type and method nodes most relevant to the task's functionality; for the dependency layer, attention is paid to the external libraries and API nodes that provide the functionality required for the task; and for the style / pattern layer, attention is paid to the specifications and pattern nodes that constrain the code implementation style. The importance of nodes within the implementation layer is further differentiated within each layer to ensure that the most critical information receives higher attention weights.

[0171] Context-dependent attention modulation is applied to dynamically adjust the attention calculation process based on the specific context of the project, such as increasing attention to performance-related patterns in projects with high performance requirements. Attention regularization is implemented to prevent excessive focus on a few nodes, ensuring the diversity and robustness of the representation. Through these mechanisms, a hierarchical weight distribution is calculated, which accurately quantifies the relevance and importance of information at each level to the current task.

[0172] Finally, based on the hierarchical weight distribution, the node representations of each level are weighted and summed. A hierarchical representation pooler is implemented, which first aggregates node representations within each level to generate hierarchical representation vectors. Importance-guided pooling is applied, performing a weighted average based on the attention weights of nodes to ensure that key nodes have a greater influence on the hierarchical representation. A structure-aware pooling function is specifically designed to preserve structural information within the hierarchy, such as code hierarchy or dependent network topology.

[0173] Inter-layer weighted fusion is performed by summing the representations of the three main layers using the calculated hierarchical weight distribution. Dynamic feature enhancement is implemented by dynamically adjusting the importance of feature dimensions according to the hierarchical weights, highlighting the feature dimensions most relevant to the current task. A nonlinear fusion transformation is applied by using a multilayer perceptron to perform a nonlinear transformation on the weighted summation result, enhancing the expressive power of the representation.

[0174] Context vector normalization is performed to stabilize the norm and distribution of the context vectors through appropriate normalization techniques, facilitating subsequent processing. Finally, dimensionality reduction and optimization are executed using techniques such as principal component analysis or autoencoders to reduce vector dimensionality while preserving key information, thereby improving processing efficiency. The resulting context vector representation is highly condensed and information-rich, accurately capturing the project context features relevant to the current task, providing strong guidance for subsequent code generation.

[0175] Through this series of meticulously designed processing steps, the structured information within the multi-layered knowledge graph can be fully extracted to generate high-quality context vector representations. These representations not only incorporate the project's technical characteristics and coding style but also highlight information most relevant to the current task, providing precise contextual guidance for code generation. This method, based on graph neural networks and attention mechanisms, generates code that both meets functional requirements and naturally integrates into the project's style, significantly improving the practicality and acceptability of the generated code.

[0176] Prior to the processing flow of the context-aware module, this embodiment of the invention further includes the following steps: Based on the structured representation of the project codebase, a code dependency graph is constructed. The vertices of the code dependency graph represent classes, traits, objects, and methods, and the edges represent call, inheritance, and containment relationships. A weight reflecting the dependency strength is assigned to each edge to obtain a weighted code dependency graph. A fixed-parameter algorithm is applied to the weighted code dependency graph, using tree decomposition technology to decompose the weighted code dependency graph into a tree structure to limit the range of nodes that need to be traversed. Dynamic programming is used to accelerate the solution of subproblems to find a set of multi-way cut nodes. The set of multi-way cut vertices is a set of key nodes that, after removal, can divide the weighted code dependency graph into multiple unconnected components that do not contain preset terminal nodes, thus obtaining a set of multi-way cut nodes. Based on the vertex multi-way cut set, identify the connected subgraphs in the weighted code dependency graph that are not segmented by the vertex multi-way cut set, enumerate the combinations of the connected subgraphs, and evaluate the cohesion and complexity of each connected subgraph through information entropy and modularity metrics to obtain an unsegmented component set; based on the vertex multi-way cut set and the unsegmented component set, establish an inter-component relationship model, which includes the dependencies between components, the information flow of data transmitted between components, and the control flow of the execution process across components to obtain an inter-component relationship model; integrate the vertex multi-way cut set, the unsegmented component set, and the inter-component relationship model into the multi-layered context knowledge graph, adding a functional module layer and a key interface layer to obtain an enhanced context knowledge graph.

[0177] Specifically, based on the structured representation of the project codebase, a code dependency graph is constructed and weights are assigned to the edges. This process first initializes the code dependency analyzer, extracting dependency information from the project's structured representation. The system creates a code dependency graph where vertices represent code entities (classes, traits, objects, and methods) and edges represent dependencies between entities. Three core dependency relationships are identified through static analysis: call relationships (calls between methods), inheritance relationships (extension and implementation of classes and traits), and containment relationships (class members and inner classes). For call relationships, not only direct calls are identified, but indirect calls, such as those implemented through callbacks, closures, or function parameter passing, are also identified through control flow analysis.

[0178] A multi-dimensional weight allocator is implemented to assign weights reflecting the strength of dependencies to each edge in the dependency graph. A multi-factor weight calculation model is adopted, comprehensively considering multiple dimensions of dependency characteristics: call frequency (the number of times a method is called), call importance (the criticality of the called method to the caller's functionality), structural tightness (the tightness of the structural relationships between entities), and change coupling (the common change patterns of entities in the version history). The system also implements "context-sensitive weight adjustment," adjusting weights according to the context of the call, such as giving higher weights to calls in core business logic than to calls in exception handling paths. Finally, the scores of each dimension are combined using a weighted average method to obtain the comprehensive weight value of each edge.

[0179] A fixed-parameter tractable (FPT) algorithm is applied to a weighted code dependency graph to find the set of key nodes that can partition the graph into multiple components, i.e., the vertex multiway cut. Traditional multiway cuts typically cut edges and require given terminal nodes. However, this invention, considering the characteristics of code dependency graphs, sets the core business classes or entry methods in the codebase as preset terminal nodes. First, tree decomposition technology is used to transform the complex weighted code dependency graph into a tree structure to limit the range of nodes that need to be traversed. Then, based on this tree decomposition structure, dynamic programming is used to accelerate the solution of subproblems to find the vertex multiway cut. The vertex multiway cut is defined as: a set of key nodes that, after removal, can partition the weighted code dependency graph into multiple unconnected components that do not contain the preset terminal nodes. By introducing a parameterized control mechanism, key parameters such as the number of target components, the maximum number of cut vertices, the minimum component size, and component balance can be adjusted to achieve a balance between different architectural goals.

[0180] Based on a set of multi-way cut vertices, unsegmented connected subgraphs in the weighted code dependency graph are identified, and the cohesion of each subgraph is evaluated. All cut vertices and their associated edges are temporarily removed, and then a depth-first search algorithm is used to identify all connected components in the remaining graph, each corresponding to a potential functional module. The cohesion of each connected subgraph is evaluated from multiple dimensions: functional cohesion (whether nodes within the subgraph serve related functions), structural cohesion (the tightness of the subgraph's topological structure), and information entropy (a measure of the subgraph's complexity and orderliness). Code information entropy is a metric for code complexity, reflecting the uncertainty of the code structure; lower information entropy generally indicates higher design quality.

[0181] The process involves enumerating and evaluating component compositions, exploring different combinations of connected subgraphs, and finding the optimal module partitioning. A heuristic search strategy is employed, defining a composition quality score that comprehensively considers the overall cohesion of the combined components, the complexity of inter-component interfaces, and the balance between component sizes. Special attention is paid to the principle of minimizing interfaces, prioritizing combinations with fewer and less complex inter-component interfaces, resulting in clearer architectural boundaries.

[0182] Based on a set of multi-way cutpoints and a set of uncut components, a relationship model between components is established. Three key relationships are identified: dependencies (functional dependencies between components), information flow (data transfer between components), and control flow (the flow of execution across components). The cutpoint nodes connecting different components are analyzed to determine the direction, type, and strength of dependencies; data flow analysis techniques are used to track the data transfer paths between components; and a system-level control flow graph is constructed to analyze how the execution flow moves from one component to another.

[0183] Finally, the multi-way cutpoint set, the uncut component set, and the component relationship model are integrated into a multi-layered contextual knowledge graph, adding a functional module layer and a key interface layer. "Functional module nodes" are created to represent each component in the uncut component set, and "key interface nodes" are created to represent each cutpoint in the multi-way cutpoint set. Relationships are established between the new layers and existing layers, such as the "usage" relationship between functional modules and dependent libraries, and the "compliance" relationship between functional modules and style features. Dependency edges, information flow edges, and control flow edges are created between functional module nodes to represent the interaction methods between components. "Architectural pattern nodes" are created to represent identified architectural patterns, providing high-level design information for the knowledge graph. The enhanced knowledge graph contains a full-spectrum contextual perspective, from micro-code details to macro-architectural design, greatly improving the system's contextual understanding capabilities.

[0184] Step S3: Fuse the requirement semantic vector and the context vector representation to obtain a joint vector representation. Based on the joint vector representation, use a pre-trained Scala code generation model to generate a code framework and core code. Generate multiple candidate code implementation schemes through a beam search algorithm and select the optimal scheme to obtain the optimal code scheme.

[0185] In one embodiment of the present invention, the step of fusing the requirement semantic vector and the context vector representation to obtain a joint vector representation, and generating a code framework and core code based on the joint vector representation using a pre-trained Scala code generation model, generating multiple candidate code implementation schemes through a beam search algorithm and selecting the optimal scheme to obtain the optimal code scheme, includes: calculating the correlation between the requirement semantic vector and the context vector representation using a cross-attention mechanism to obtain a correlation weight, and performing vector fusion based on the correlation weight to obtain the joint vector representation; loading and initializing a pre-trained Scala code generation model using a Transformer architecture based on the joint vector representation, and using the pre-trained Scala code generation model... Based on the functional intent and input / output type information in the joint vector representation, a code framework including method signatures, class definition structures, and import statements is generated to obtain the code skeleton. Based on the code skeleton and the functional description and constraints in the joint vector representation, combined with the syntax features and programming paradigm of the Scala language, the core code that implements the main function is generated to obtain the functional code. Using a beam search algorithm, K generation paths are initialized for the functional code, and multiple generation paths are maintained through iterative expansion and probability evaluation. K candidate code implementation schemes are generated according to the probability distribution of the language model to obtain a candidate scheme set. For each candidate code implementation scheme in the candidate scheme set, the language model probability, type matching degree, and performance score are calculated respectively, and a comprehensive score is calculated based on preset weight coefficients. The scheme with the highest comprehensive score is selected to obtain the optimal code scheme.

[0186] Specifically, the semantic vectors of the requirements and the context vectors are deeply fused. Then, based on the fused joint representation, a pre-trained model is used to generate code, and the optimal solution is selected through an intelligent search strategy. This process not only considers the matching degree between functional requirements and project context, but also utilizes advanced language modeling techniques and scoring mechanisms to ensure the high quality and usability of the generated code.

[0187] First, based on the demand semantic vector and context vector representations, the relevance between the two vectors is calculated using a cross-attention mechanism. The cross-attention mechanism is a neural network structure that calculates the relevance between two different sets of representations, allowing one set of representations to selectively focus on relevant elements in another set. The demand semantic vector is used as the query vector, and the components of the context vector representation are used as the key and value vectors. Multi-angle cross-attention is implemented by using multiple parallel attention heads to calculate the matching degree between the demand and the context from different perspectives. Each attention head calculates the dot product similarity between the query vector and the key vector, applies a softmax function to normalize the results to obtain attention weights, and then uses these weights to perform a weighted summation of the value vectors.

[0188] A feature-context fit evaluator was specifically designed, which analyzes the compatibility of functionalities in the requirements with implementation patterns within the project context. It performs technology stack adaptability analysis, assessing the degree of fit between the technical requirements of the requirements and the project's existing technology stack, identifying potential technical conflicts or integration challenges. Furthermore, it implements style consistency scoring, calculating the consistency between the requirement implementation method and the project's coding style, ensuring that the generated code seamlessly integrates into the project. These analyses collectively generate detailed relevance weights, reflecting the degree of fit between the requirements and various aspects of the context.

[0189] Next, vector fusion is performed based on the calculated relevance weights. A weighted feature fusion unit is implemented, which integrates requirement features and context features into a unified representation based on relevance weights. A dynamic fusion strategy is adopted, adaptively adjusting the fusion method according to the requirement type and context complexity. For requirements highly relevant to the context, the weights of context features are enhanced; for relatively independent innovative requirements, more original requirement features are retained. Feature complementarity enhancement is also implemented, identifying complementary feature dimensions in the requirement and context, and strengthening these dimensions during the fusion process to form a more comprehensive joint representation.

[0190] A nonlinear integration transformation is applied, using a multi-layer neural network to nonlinearly transform the fused features, capturing the complex interaction patterns between requirements and context. Information density balance is achieved to ensure that key requirement and contextual information are not diluted during the fusion process, maintaining sufficient representation strength. Finally, vector normalization is performed to stabilize the numerical range of the joint vector through appropriate normalization techniques, facilitating subsequent model processing. These steps collectively generate a high-quality joint vector representation that simultaneously encodes the functional intent of the requirement and the contextual features of the project, providing comprehensive guidance for code generation.

[0191] Based on joint vector representation, a pre-trained Scala code using the Transformer architecture is loaded and used to generate and initialize a model. A model loading optimizer is implemented, which intelligently manages the model loading process, optimizing memory usage and initialization speed. A parameter-precise loading strategy is employed, loading only the model layers and parameters necessary for the current task, reducing resource consumption. A model caching mechanism is also implemented, caching frequently used model configurations to accelerate the processing of repetitive tasks.

[0192] Contextual initialization is performed, adjusting the model's initial state based on the features of the joint vector representation, ensuring the model focuses on the patterns most relevant to the current task from the outset. Parameter fine-tuning activation is implemented, selectively activating parameter regions in the pre-trained model that have been fine-tuned with project-specific data based on project characteristics. Attention bias settings are also applied, pre-setting the model's attention bias based on important information in the joint vector representation, guiding the generation process to focus on key elements. These initialization steps ensure the pre-trained model fully utilizes the information in the joint vector representation, preparing it for subsequent code generation.

[0193] The initialized pre-trained Scala code generation model generates a code framework based on the functional intent and input / output type information in the joint vector representation. A functional intent analyzer is implemented, which extracts the core functional intent from the joint vector, such as data processing, algorithm implementation, or system integration. The essential type of the function is identified, such as a transformation function (converting input to output), a state management function (maintaining and operating state), or an interactive function (handling interactions with external systems), and an appropriate code organization structure is determined accordingly.

[0194] Perform type information extraction to identify type constraints for input parameters and return values ​​from the joint vector. Implement type reasoning enhancement to infer possible type structures based on functional descriptions and contextual knowledge when type information is incomplete. Apply type safety checks to ensure that the inferred type structure conforms to Scala's type system rules, avoiding type errors during generation.

[0195] Generate method signatures based on the analysis results, including method name, parameter list, and return type. Implement a naming convention adapter to generate method and parameter names that conform to the project's naming style. Determine whether to generate standalone methods or a complete class structure based on functional complexity and encapsulation requirements. For functionalities requiring class encapsulation, design an appropriate class hierarchy, including class name, constructor, member variables, and method declarations.

[0196] Analyze dependency requirements and determine necessary import statements. Implement a dependency resolver, which analyzes the required external libraries and internal project dependencies, generating corresponding import statements. Pay special attention to Scala-specific import mechanisms, such as selective imports, renamed imports, and implicit imports, selecting the most appropriate import method based on project style and best practices. Integrate the generated method signatures, class structure, and import statements into a complete code skeleton, providing a structural framework for subsequent core code implementation.

[0197] Based on the functional descriptions and constraints in the code skeleton and joint vector representation, and combined with the features and programming paradigms of the Scala language, the core code implementing the main functionality is generated. A functional decomposition engine is implemented, which breaks down complex functions into relatively independent sub-functions, facilitating modular implementation. A combined top-down and bottom-up implementation strategy is adopted, first outlining the main logical flow and then filling in the specific implementation details.

[0198] When choosing a programming paradigm, make an appropriate choice between functional, object-oriented, and imperative programming paradigms based on functional characteristics and project style. Pay particular attention to Scala's hybrid paradigm features, combining the advantages of multiple paradigms where appropriate, such as using immutable data structures and higher-order functions to implement functional logic while leveraging type systems and encapsulation for object-oriented organization. Also consider performance and safety constraints, applying specific optimization patterns or safety mechanisms where necessary.

[0199] Generate code to handle edge cases and exceptional conditions. Implement a robustness enhancer, which analyzes possible boundary values ​​and exceptional situations in the functional input and generates corresponding processing logic. Employ Scala's standard error handling methods, such as using the Option type to handle potentially missing values, the Either type to handle possible erroneous results, or using exception handling mechanisms to deal with exceptional situations. Integrate the main flow logic and edge case handling to form complete functional code. This code implements the core functionality of the requirement and handles various possible exceptional situations.

[0200] A beam search algorithm is employed to generate multiple candidate implementation schemes for the functional code. Beam search is a heuristic search algorithm that retains K most promising candidate paths at each step, balancing the efficiency of greedy search with the comprehensiveness of exhaustive search. An adaptive beam width controller is implemented, which dynamically adjusts the beam width (K value) based on task complexity and diversity requirements. A narrower beam width is used to improve efficiency in simple tasks, while a wider beam width is used to increase diversity in complex tasks.

[0201] Initialize K generation paths for the functional code, each path representing a possible direction for code generation. The system implements diversity seed generation to ensure that the initial paths have sufficient diversity to cover different implementation styles and technical solutions. By iteratively expanding these paths, at each step, generate the next possible code element (such as a statement, expression, or method call) based on the probability distribution of the language model. Apply context-sensitive decoding to ensure that the generated code elements are consistent with the context of the current path, maintaining syntactic correctness and semantic coherence.

[0202] After each iteration, all possible expansion paths are evaluated and ranked, and the K paths with the highest probabilities are retained. A path diversity maintainer is implemented, which considers not only probability but also the diversity between paths when selecting paths, avoiding excessive similarity among the K paths. After all iterations are completed, K complete candidate code implementation schemes are obtained, each scheme being a possible functional implementation, forming a candidate scheme set.

[0203] A comprehensive evaluation of each candidate code implementation in the candidate solution set is performed to select the optimal solution. A multi-dimensional scoring system is implemented to evaluate the quality of candidate solutions from multiple aspects. The language model probability of each solution is calculated, which reflects the naturalness of the code in terms of syntax and usage. A type matching evaluator is implemented, which analyzes the type correctness and type usage accuracy of the code to ensure that there are no type errors or unnecessary type conversions.

[0204] It also performs performance scoring, analyzing the code's time and space complexity. A static performance analyzer is implemented, which identifies common performance anti-patterns, such as unnecessary object creation, redundant computations, or unoptimized collection operations, and evaluates code performance accordingly. Project-specific performance requirements, such as response time constraints or memory usage limitations, are considered and weighted appropriately in the scoring.

[0205] The system calculates a comprehensive score for each candidate solution based on preset weighting coefficients. It implements context-adaptive weight adjustment, adjusting the weights of different dimensions according to the project nature and the importance of requirements. For example, in performance-critical projects, performance scores receive higher weight; in type-safety-critical projects, type matching receives higher weight. The system performs the final comparison and selection, determining the solution with the highest comprehensive score as the optimal code solution.

[0206] The optimal solution undergoes final verification to ensure it meets all functional requirements and constraints. A requirements coverage validator is implemented, which checks if the code implements all specified functionalities and conforms to all constraints. Final syntax and type checks are performed to ensure the code is completely correct in terms of syntax and type system. A final evaluation of readability and style consistency is also conducted to ensure the code conforms to the project's coding style and best practices.

[0207] Through this series of meticulously designed steps, the semantics of requirements and contextual knowledge are deeply integrated to generate diverse code implementation schemes based on advanced pre-trained models. A comprehensive evaluation mechanism then selects the optimal solution. This approach ensures both the functional correctness and technological advancement of the code, while also guaranteeing consistency and integration with the project style, significantly enhancing the practical value and engineering applicability of automatically generated code.

[0208] In one embodiment of the present invention, the step of loading and initializing a pre-trained Scala code generation model using the Transformer architecture based on the joint vector representation, and using the pre-trained Scala code generation model to generate a code framework including method signatures, class definition structures, and import statements based on the functional intent and input / output type information in the joint vector representation to obtain a code skeleton, includes: loading a pre-trained Scala code generation model using the Transformer architecture based on the joint vector representation, and loading the model parameters of the pre-trained Scala code generation model adjusted through supervised learning on the Scala codebase to complete model initialization, obtaining an initialized generation model; extracting functional intent information from the joint vector representation, and using the initialized generation model to generate method signatures or class definition structures, obtaining a code structure framework; extracting input / output type information from the joint vector representation, and using the initialized generation model to determine the parameter list and return type based on the input / output type information, obtaining type-annotated method signatures; generating necessary import statements based on the dependency information in the joint vector representation, obtaining a set of import statements; and integrating the code structure framework, the type-annotated method signatures, and the set of import statements to obtain the code skeleton.

[0209] Specifically, based on the fused joint vector representation, a structured code skeleton is generated through a pre-trained Transformer model. This process includes key steps such as model initialization, method structure generation, type annotation, and import statement generation, laying a solid structural foundation for subsequent detailed code implementation.

[0210] First, based on joint vector representation, a pre-trained Scala code generation model using the Transformer architecture is loaded. A model selector is implemented, which chooses the most suitable pre-trained model from a model library based on task characteristics and project requirements. The complexity of the task, domain characteristics, and technical requirements are analyzed to select a specialized model that performs well on similar tasks. For tasks in a specific domain, a specialized model pre-trained on code within that domain is preferred; for general tasks, a general model pre-trained on a broad codebase is selected.

[0211] While loading the model, parameter organization optimization is performed. This technique reorganizes the storage layout of model parameters to better suit the current hardware architecture and improve computational efficiency. Mixed-precision loading is implemented, selectively using different numerical precisions based on the importance of different parameter layers, reducing memory usage while maintaining model performance. A progressive loading strategy is also applied, loading the core layers and key parameters first, enabling the model to quickly begin initial processing, while the full set of parameters continues to be loaded in the background.

[0212] Load the pre-trained model and tune its parameters using supervised learning on a Scala codebase. Supervised tuning refers to the process of fine-tuning a pre-trained model using a labeled dataset (in this case, high-quality Scala code) to better adapt the model to a specific domain or task. Pay special attention to Scala-specific parameter areas, such as network layers that handle Scala syntax features, type systems, and functional programming patterns, ensuring that parameters in these areas are fully loaded and correctly initialized.

[0213] Perform parameter consistency verification to check if the loaded parameters are complete, consistent, and match the model architecture. Implement distributed parameter synchronization to ensure model parameter consistency in multi-GPU or distributed environments, avoiding inconsistencies in results due to hardware differences. Also perform initial inference testing using simple test inputs to verify the model's basic functionality and ensure the initialization process is error-free. These steps complete the model initialization, resulting in an initialized generative model that can be used for code generation.

[0214] Extract functional intent information from the joint vector representation and generate method signatures or class definition structures using an initialized generative model. Implement a semantic vector parser, which interprets functional intent information from different dimensions of the joint vector. Through dimensional importance analysis, identify the set of dimensions in the vector that contribute most to the functional intent and focus on the information in these dimensions. Perform pattern matching recognition, comparing the joint vector with known functional pattern vectors to identify the functional category and implementation pattern of the current task.

[0215] Based on the identified functional intent, the most suitable code organization structure is determined. A "structure decision engine" is implemented, which decides whether to use different structures such as independent methods, traits, regular classes, or singleton objects based on functional characteristics. The system considers multiple factors, such as the functional state management requirements (stateless functions are suitable for independent methods, stateful operations are suitable for class encapsulation), reuse patterns (reusable interface definitions in multiple places are suitable for traits, single-function functions are suitable for regular methods), and instantiation requirements (singleton functions are suitable for objects, multi-instance functions are suitable for classes).

[0216] Generative models are used to create an initial code structure definition. A progressive generation strategy is implemented, first generating the outermost structure (such as class or trait declarations), and then gradually adding internal elements (such as method declarations and member variables). A consistency maintenance mechanism is applied to ensure that the generated parts are consistent in naming, style, and organization, conforming to the project's coding standards. Structural integrity checks are also performed to verify that the generated structure contains all necessary components required for functionality, such as class constructors, necessary member variables, or interface methods. These steps generate the structural framework of the code, providing an organizational skeleton for subsequent detail filling.

[0217] This process extracts input and output type information from the joint vector representation and uses this information to determine the parameter list and return type using an initialized generative model. A type information extractor is implemented, focusing on interpreting the type constraints and type structure information encoded in the joint vector. Vector subspace projection is applied to project the joint vector onto a specific type representation subspace, extracting type-related features. Type pattern decoding is also performed, mapping the extracted features back to specific Scala type expressions, such as primitive types, parameterized types, or composite types.

[0218] Based on the extracted type information, determine the method's parameter list. Implement a parameter design optimizer, which designs the most reasonable parameter structure based on the number, type, and usage patterns of parameters. Considering Scala's multi-parameter list capabilities, use currying where appropriate to group logically related parameters into different parameter lists, enhancing readability and reusability. Pay special attention to implicit parameters, deciding whether to use them to pass contextual information or type evidence based on functional requirements and project style.

[0219] Based on functional semantics and type constraints, determine the method's return type. Implement a return type inferrer, which analyzes the function's operational logic and expected results to infer the most appropriate return type. Considering the richness of Scala's type system, select the most precise and expressive type representation, such as using parameterized types (e.g., Option[T], Future[T]) to express potentially missing or asynchronous results, and using sealed trait hierarchies to express restricted result sets. Also consider the use of type parameters, introducing generic parameters where appropriate to improve code flexibility and reusability.

[0220] By combining the parameter list and return type, a complete method signature is formed. Type safety checks are applied to verify the consistency and completeness of the generated signature across the type system, ensuring there are no type mismatches or insufficient type information. Signature optimization is also performed, simplifying unnecessary type declarations, reducing redundant annotations through type inference, while maintaining explicit type annotations in critical positions, balancing simplicity and clarity. These steps generate a complete and expressive method signature, providing precise type constraints for subsequent method implementation.

[0221] Based on dependency information in the joint vector representation, the necessary import statements are generated using the initialized generative model. A dependency requirement analyzer is implemented, which analyzes the required external libraries and internal project components. Encoded dependency graph information is extracted from the joint vector, identifying the dependency nodes most relevant to the current task. Usage pattern prediction is performed to predict the classes and methods the code might use, inferring the required imports.

[0222] Analyze the project's dependency management structure to identify available dependency libraries and internal packages. Implement an import path resolver, which maps identified dependencies to specific import paths and handles package and class name resolution. The system considers Scala's import mechanism features, such as package objects, default imports, and implicit imports, to ensure the completeness and correctness of import statements.

[0223] Optimize the organization and formatting of import statements. Implement an import optimization strategy that organizes import statements based on project style and best practices. Consider factors such as import grouping (e.g., separating standard library, third-party library, and internal project imports), sorting (e.g., alphabetical or importance-based), and formatting (e.g., single-line or multi-line imports). Also apply import simplification techniques to avoid unnecessary full-package imports, favoring imports of only the necessary classes and methods, thus improving code readability and maintainability.

[0224] It handles Scala-specific import features such as selective imports and renamed imports. It allows importing specific members using curly braces or all members using underscores as needed. It also resolves naming conflicts using `import ... as ...`, ensuring clarity and unambiguity. Special attention is paid to implicit imports, ensuring necessary implicit conversions and values ​​are imported correctly, and supporting implicit operations in the code. These features generate a complete set of import statements, providing the necessary dependency support for the code.

[0225] The code structure framework, method signatures annotated with type annotations, and import statement sets are integrated to obtain a complete code skeleton. A code component integrator is implemented, which is responsible for combining the independently generated code parts into structurally complete code units. Structural consistency verification is applied to check the syntactic and organizational correctness of the integrated code structure, ensuring that the parts match and are compatible with each other.

[0226] Import placement optimization is performed, ensuring import statements are placed appropriately within the code, following Scala conventions and the project's specific guidelines. Code formatting is also implemented, adjusting indentation, line breaks, and spaces according to the project's formatting rules to ensure visual consistency and readability. Finally, the system performs syntax validation to ensure the integrated code skeleton is syntactically correct and passes the compiler's initial checks.

[0227] Furthermore, documentation comment reservations are implemented, adding placeholder comments at the declarations of key code elements (such as classes, traits, and methods) to prepare for subsequent documentation improvements. The requirements of Scala documentation generation tools (such as Scaladoc) are considered to ensure that comment placement and formatting conform to specifications. The final generated code skeleton is a complete structural unit, containing necessary import declarations, type definitions, and method signatures, providing a clear framework and accurate type constraints for subsequent functional code implementation.

[0228] This meticulously designed code skeleton generation process generates structurally sound, type-safe code skeletons that conform to the project style, based on the rich semantic information in joint vector representations. These skeletons not only reflect the core intent and type requirements of the functionality but also consider the characteristics of the Scala language and project specifications, laying a solid foundation for subsequent functional code implementation. The generated code skeleton serves as both a structural guide for functional implementation and a type-safe framework, which is crucial for improving the quality and maintainability of the final code.

[0229] In one embodiment of the present invention, based on the functional description and constraints in the code skeleton and the joint vector representation, and combined with the syntax features and programming paradigm of the Scala language, core code implementing the main function is generated to obtain functional code, including: decomposing the functional requirements to be generated into relatively independent subtasks based on the functional module layer and key interface layer in the enhanced context knowledge graph, each subtask corresponding to an uncut component or cross-component function in the uncut component set, to obtain a subtask set; identifying nodes connecting different uncut components in the vertex multi-way cut set, generating interface code corresponding to the nodes, the interface code defining the interaction contract between different functional modules, to obtain an interface code set; based on the The set of uncut components is described. For each uncut component in the set, corresponding implementation code is generated based on the internal dependency structure and functional characteristics of the uncut component, resulting in a module internal implementation code set. Based on the component relationship model and the interface code set, code for handling inter-module collaboration is generated. The code for handling inter-module collaboration includes dependency injection mechanism, event propagation logic, asynchronous communication implementation, and error propagation handling, resulting in inter-module collaboration code. The interface code set, the module internal implementation code set, and the inter-module collaboration code are assembled into a complete implementation. The type consistency and semantic consistency of cross-module references are checked to ensure type safety and semantic matching between different modules, resulting in the functional code.

[0230] Specifically, based on the functional descriptions and constraints in the code skeleton and joint vector representation, and combined with Scala language features, the core code that implements the main functionality is generated. This process fully utilizes the architectural information in the enhanced contextual knowledge graph to achieve architecture-aware modular code generation.

[0231] First, based on the functional module layer and key interface layer in the enhanced contextual knowledge graph, the functional requirements to be generated are decomposed into relatively independent sub-tasks. A functional decomposition engine is implemented, which analyzes the mapping relationship between functional requirements and the existing architecture, identifying the degree of association between the requirements and each component in the set of uncut components. A semantic similarity matrix is ​​used to calculate the similarity between the functional requirements and the functional descriptions of each uncut component, determining which components the functional requirements should be mapped to. For functional requirements spanning multiple components, their natural decomposition points are identified, typically corresponding to interface boundaries or data transformation nodes between components.

[0232] The functional module layer, located within the context knowledge graph, describes the functional grouping of the software system. It categorizes and organizes system functions according to their responsibilities and logical relevance, forming relatively independent functional units. Nodes at this layer represent functional modules, and edges represent dependencies and interactions between modules. The functional module layer is constructed using an automatic module identification algorithm. This algorithm analyzes the dependency network of the project code, uses community detection technology to identify cohesive functional clusters, and considers namespace structure and structural cues provided by developer annotations. The key interface layer, on the other hand, describes the communication bridges between modules within the context knowledge graph. It defines the methods and protocols for interaction between functional modules. Nodes at this layer represent interface definitions or service boundaries, and edges represent interface provision and usage relationships. The key interface layer is constructed using interface feature extraction technology. This technology identifies abstract types, trait declarations, and frequently cross-package referenced public methods in the code, analyzes their uses and calling patterns, and determines their importance as bridges between modules.

[0233] The system generates subtasks, creating a description for each relevant component, including the functionalities the component is responsible for, input / output data formats, and processing constraints. Priorities and dependencies are assigned to each subtask, a subtask dependency graph is constructed, and the execution order and parallelization possibilities of the subtasks are determined. For subtasks requiring cross-component collaboration, the system specifically marks their collaboration requirements and interface requirements, providing a basis for subsequent interface code generation. The final set of subtasks forms a structured task decomposition scheme, with each subtask having clearly defined responsibility boundaries and execution context.

[0234] Next, the system identifies nodes connecting different uncut components within the multi-way cut node set and generates corresponding interface code. An interface extractor is implemented, which analyzes the functional characteristics of the cut nodes and the types of connected components to extract key interface features. Considering the stability and versatility of the interfaces, interface definitions are designed to meet long-term evolution requirements. Using the contract design pattern, explicit preconditions, postconditions, and invariants are defined for each interface to ensure the safety and predictability of interface usage.

[0235] The generated interface code uses the most suitable Scala interface representation, which may be a trait (suitable for defining behavioral contracts), an abstract class (suitable for providing partial implementation), or a case class (suitable for defining data structures). The system adds detailed ScalaDoc comments to each interface method, explaining the method's purpose, parameter meanings, return value descriptions, and exception conditions. Special attention is paid to interface type safety, using generic parameters and type constraints appropriately to ensure type consistency across different contexts. For interfaces that need to handle error conditions, error handling strategies are explicitly defined, such as using Option, Either, or custom error types. The final generated set of interface code defines clear interaction contracts between functional modules, laying the foundation for modular implementation.

[0236] Based on the set of unsplit components, corresponding implementation code is generated for each component. A component feature analyzer is implemented, which analyzes the internal dependency structure, functional characteristics, and quality features of the unsplit components to determine the most suitable implementation strategy. The system identifies the main processing mode of the components, which may be data transformation, business logic, state management, or external integration, and selects different implementation modes for different types of components. A cohesion-driven implementation strategy is adopted to ensure that the generated code maintains high cohesion, with related functions concentrated in the same class or the same method.

[0237] Implement a Scala feature optimizer to select the most suitable Scala language features and functional programming techniques based on component characteristics. For data-intensive components, prioritize immutable collections, higher-order functions, and collection operations; for state-management components, consider using the Actor model or functional state management patterns; for complex business logic components, simplify conditional processing using pattern matching and algebraic data types. Pay special attention to error handling strategies, selecting the most suitable error handling mode based on the component's robustness requirements, such as error accumulation, fail-fast, or error recovery. Also consider performance optimization opportunities, applying techniques such as lazy evaluation, tail recursion optimization, or parallel collections on the critical path. The final generated set of module internal implementation code contains the complete functional implementation of each component, and each implementation is optimized for component characteristics.

[0238] Based on the component relationship model and interface code set, code for handling inter-module collaboration is generated. A module collaboration generator is implemented, which analyzes dependencies, information flow, and control flow between components and designs collaboration patterns between modules. For dependency injection requirements, a dependency injection code generator is implemented, generating the most suitable dependency injection code based on the project style, potentially using constructor injection, the cake pattern, or a dependency injection framework.

[0239] To address event propagation requirements, the system generates event bus or observer pattern implementations to handle loosely coupled communication between components. The system pays particular attention to asynchronous communication scenarios, generating asynchronous call code based on Future / Promise or Actor message passing code to ensure non-blocking interaction. The system also handles error propagation paths, implementing error transformation and cross-module error handling logic to ensure system stability under abnormal conditions. For complex cross-module workflows, the system generates coordinator components or state machine implementations to manage operation sequences across multiple modules. A transaction boundary identifier is implemented, adding transaction management code at appropriate locations to ensure the atomicity and consistency of cross-module operations. The final generated inter-module collaboration code handles all the complexities of cross-module interactions, ensuring seamless collaboration between modules.

[0240] Finally, module assembly and consistency checks are performed, assembling the interface code set, the module's internal implementation code set, and the inter-module collaboration code into a complete implementation. The system implements a type consistency checker, which verifies type compatibility across module references, ensuring there are no type mismatches between interfaces and implementations. The system checks the consistency of generic parameter passing, paying particular attention to runtime type mismatch risks that may arise from type erasure.

[0241] It also implements a semantic consistency analyzer to check whether the semantic conventions between different modules are consistent, such as unit assumptions, null value handling strategies, and exception handling conventions. It pays particular attention to contract violation risks, verifying whether the implementation code meets the preconditions and postconditions defined in the interface. It executes integration test generation, generating simple test code for interactions between modules to verify the correctness of the integrated functionality. Furthermore, it identifies performance bottlenecks by analyzing the frequency and data volume of cross-module calls, identifying potential performance bottlenecks, and providing optimization suggestions.

[0242] After comprehensive assembly and inspection, complete functional code is generated. This code not only implements the required functionality but also adheres to the project's architectural design and modular division. The generated code features clear module boundaries, standardized interface definitions, and optimized in-module implementations, enabling seamless integration into existing projects. Furthermore, the modular structure facilitates subsequent maintenance and expansion, conforming to best practices in software engineering.

[0243] This architecture-based modular code generation method produces high-quality code that meets both functional requirements and project architectural design. It is particularly suitable for implementing complex functionalities, breaking down complex requirements into manageable modules and ensuring these modules collaborate correctly to form a complete solution. The generated code not only maintains high cohesion and low coupling within modules but also adheres to clear interface agreements and collaboration patterns between modules, reflecting sound software architecture design principles.

[0244] In one embodiment of the present invention, the step of employing a beam search algorithm to initialize K generation paths for the functional code and maintain multiple generation paths through iterative expansion and probability evaluation, and generating K candidate code implementation schemes based on the probability distribution of the language model, to obtain a candidate scheme set, includes: based on the functional code, initializing the beam search algorithm, setting the beam width to K, creating K initial generation paths, to obtain an initial path set; for each generation path in the initial path set, using the pre-trained Scala code generation model to calculate the probability distribution of the next code tag, to obtain a tag probability distribution; based on the tag probability distribution, selecting several candidate tags with the highest probability for each generation path for path expansion, to obtain an expanded path set; calculating the cumulative probability of each path in the expanded path set, selecting the K paths with the highest cumulative probability as the current retained paths, to obtain an updated path set; repeatedly performing the calculation of the tag probability distribution, the path expansion based on the tag probability distribution, and the path selection based on the cumulative probability until all retained paths generate complete code implementations or reach the maximum length limit, to obtain the candidate scheme set.

[0245] Specifically, a beam search algorithm is used to initialize multiple generation paths for the functional code. Through iterative expansion and probabilistic evaluation, the most promising path is retained, ultimately generating multiple candidate code implementations. This method maintains diversity during the generation process while leveraging the probabilistic guidance of a language model to ensure that the generated code is both diverse and of high quality.

[0246] First, the beam search algorithm is initialized based on the functional code, setting the beam width to K and creating K initial generated paths. Beam search is a heuristic search algorithm that retains K most promising candidate paths at each step, balancing the efficiency of greedy search with the comprehensiveness of exhaustive search. Beam width is a key parameter in the beam search algorithm, determining the number of paths retained at each step. A larger beam width allows for exploring more possibilities but also requires more computational resources. The beam width is dynamically adjusted based on task complexity and resource availability, using smaller K values ​​(e.g., 5-10) for simple tasks and larger K values ​​(e.g., 20-50) for complex tasks to ensure sufficient coverage of the search space.

[0247] A diverse initial path generator is implemented, responsible for creating a set of initial paths with sufficient diversity. The system analyzes the structural characteristics of the functional code, such as control flow complexity, dependencies, and algorithm type, to identify key code variation points. Based on these variation points, different implementation strategies are designed, such as functional, imperative, iterative, or recursive implementations, ensuring that the initial paths cover different programming paradigms and implementation approaches. A performance optimization perspective is also considered, creating different initial paths that prioritize time efficiency, space efficiency, or code conciseness.

[0248] Application context initialization adjusts the distribution of initial paths based on the project's technical background and style preferences to better align with project expectations. For performance-critical projects, the system increases the proportion of performance-optimized paths; for projects emphasizing code readability, it increases the proportion of clear and concise paths; and for Scala projects emphasizing functional style, it prioritizes generating functional implementation paths. These initial paths serve as a starting point to form an initial path set, providing a diverse foundation for subsequent iterative expansions.

[0249] Next, for each generated path in the initial path set, a pre-trained Scala code generation model is used to compute the probability distribution of the next code tag. Context-sensitive probability computation is implemented, which considers the complete history of the current path and the project context to calculate more accurate conditional probabilities. The generated code tag sequence is used as input, and the model's predicted distribution for the next possible tag is obtained through forward propagation of the pre-trained model. Special attention is paid to Scala-specific syntax structures and idioms, such as matching expressions, for comprehensions, and implicit transformations, to ensure that the model's probability evaluations of these structures accurately reflect Scala programming practices.

[0250] This system implements probabilistic temperature regulation, controlling the sharpness of the probability distribution by adjusting temperature parameters. Lower temperatures (e.g., 0.5) make the probability distribution more concentrated, tending to select the highest probability markers, which is beneficial for generating more conservative code that conforms to common patterns. Higher temperatures (e.g., 1.2) flatten the probability distribution, increasing the chance of low-probability markers being selected, which is beneficial for generating more diverse and innovative code. The temperature is dynamically adjusted according to the current generation stage. Lower temperatures are used in the code framework generation stage to ensure structural correctness, while higher temperatures are used in the specific implementation generation stage to encourage diversity.

[0251] Syntax constraint filtering is performed, filtering out invalid next tokens according to Scala syntax rules to ensure the generated path always maintains syntactic correctness. A dynamically updated syntax state machine is maintained to track bracket matching, indentation levels, and block structure, eliminating token selections that would lead to syntax errors. Type-aware filtering is also applied, using static type information to constrain the selection of the next token; for example, in a context receiving an Int type parameter, a token that generates an Int type expression is preferred. These processes generate a token probability distribution under syntax and type constraints, guiding the next step of path expansion.

[0252] Based on the calculated probability distribution of the markers, several candidate markers with the highest probabilities are selected for path expansion for each generated path. An adaptive expansion factor is implemented, dynamically determining the number of expansions for each path based on the entropy of the probability distribution and the complexity of the current path. When the probability distribution is concentrated (low entropy), fewer high-probability markers (e.g., 2-3) are selected for expansion to save computational resources; when the probability distribution is dispersed (high entropy), the system selects more candidate markers (e.g., 5-10) for expansion to explore more possibilities.

[0253] To enhance diversity selection, candidate tags are chosen not only based on probability but also on their differences from already selected tags. A kernel penalty mechanism is used to penalize candidate tags that are too semantically or syntactically similar, reducing their selection probability and ensuring diversity in expansion paths. Furthermore, code context matching is implemented to evaluate the fit between candidate tags and the current code context, such as variable naming consistency, API usage consistency, and error handling pattern consistency, prioritizing tags that better fit the context.

[0254] For each selected candidate tag, a new path branch is created, and the tag is added to the end of the current path to form the expanded new path. Path state maintenance is implemented, updating relevant state information for each new path, such as syntax state, variable scope, type environment, and implemented functionalities, to ensure the continuity and consistency of path expansion. All candidate expansions of all current paths are processed to generate a set of expanded paths, which contains all possible new paths expanded from the current K paths.

[0255] Calculate the cumulative probability of each path in the expanded path set, and select the K paths with the highest cumulative probabilities as the currently retained paths. Cumulative probability is a metric for overall path quality, combining the conditional probabilities of all markers within the path. Implement a normalized cumulative probability calculation method that considers differences in path length to prevent shorter paths from gaining an unfair advantage due to fewer markers. Use the square root or logarithm of the path length as a normalization factor to calculate a length-normalized cumulative probability score, ensuring a fair comparison of paths of different lengths.

[0256] A multi-criteria ranking approach is applied, incorporating various quality metrics when evaluating paths. In addition to cumulative probability, code integrity metrics are considered to assess the path's coverage of functional requirements; structural balance metrics to evaluate the integrity and balance of the code structure, such as bracket matching and block closure; and style consistency metrics to assess the code's adherence to the project style. These metrics are weighted and combined to form a comprehensive score. The K paths with the highest comprehensive scores are selected and retained, forming an updated path set.

[0257] The process repeatedly calculates the marker probability distribution, expands the path based on the marker probability distribution, and selects the path based on the cumulative probability until a termination condition is met. It implements multi-condition termination detection, monitoring multiple termination metrics. A completeness termination condition is checked: when a path generates a complete code implementation, containing all necessary functionalities and structural elements, and the code structure is closed (e.g., all parentheses and blocks are correctly closed), the path is marked as complete. A length limit termination condition is also checked: when a path reaches a preset maximum length limit (e.g., the number of lines of code or the number of markers), the path is marked as having reached the limit.

[0258] A batch termination strategy is applied, meaning that not all paths are required to reach the termination condition simultaneously. When a path reaches the termination condition, it is moved to the candidate solution set and removed from the current path set to make room for the continued expansion of other paths. This process continues until all K slots are filled with complete candidate solutions, or the preset maximum number of iterations is reached to prevent infinite loops. Finally, all paths that have reached the complete state are collected to form a candidate solution set, which contains at most K high-quality and diverse code implementations.

[0259] This meticulously designed beam search process effectively explores a vast code generation space while maintaining a balance between diversity and quality. Compared to greedy search, beam search explores more possibilities and avoids getting trapped in local optima; compared to exhaustive search, beam search significantly improves efficiency by retaining the K most promising paths. This method is particularly well-suited for code generation tasks, generating multiple high-quality candidate solutions, providing rich options for subsequent solution evaluation and selection, and greatly improving the quality and adaptability of the final generated code.

[0260] After generating the functional code, this embodiment of the invention further includes the following steps: based on the functional code, a code generation graph is constructed, where the vertices of the code generation graph represent the code segments to be selected and their corresponding program states during the code generation process, the edges represent the transition paths of code generation, and the edge weights represent the probability or quality score of state transitions, resulting in a weighted code generation graph; the two-forest representation theory is applied to the weighted code generation graph to decompose it into multiple spanning tree forests, each two-forest containing several unconnected tree structures, the root node of each tree representing a key code decision point, and each tree corresponding to a cohesive code. Generate logical fragments to obtain a binary forest set; based on the binary forest set, design a binary forest sampling algorithm based on Markov chain Monte Carlo, define the transition kernel function between the two forests, apply the Metropolis-Hastings algorithm to calculate the acceptance probability of state transitions, and sample N representative binary forests from the code generation space through random walks to obtain a sampled binary forest set; apply deterministic point processing technology to the sampled binary forest set, calculate the similarity matrix between samples in the sampled binary forest set, maximize the determinant of the sample set to ensure diversity, and apply the k-DPP algorithm to sample the binary forests from the sampled binary forest set. The following steps are taken: First, select the k most representative samples from the sampled forest set to obtain a diverse candidate solution set. For each code implementation scheme in the diverse candidate solution set, construct the Markov chain transition matrix corresponding to the code implementation scheme. Calculate the stationary distribution and average first-arrival time matrix of the Markov chain transition matrix, and calculate the Kemoni constant based on the stationary distribution and the average first-arrival time matrix to obtain a Kemoni constant score. Based on the Kemoni constant score, use static analysis tools to perform time and space complexity analysis on each code implementation scheme in the diverse candidate solution set to obtain a code performance prediction score. Calculate the abstract syntax tree edit distance between any two code implementation schemes in the diverse candidate solution set to obtain a structural dissimilarity matrix. Based on the structural dissimilarity matrix, calculate the average structural dissimilarity of each code implementation scheme with other schemes to obtain a diversity index score. Based on the Kemoni constant score, the code performance prediction score, and the diversity index score, perform a weighted sum according to preset weight coefficients to calculate the comprehensive quality score of each code implementation scheme in the diverse candidate solution set. Select the code implementation scheme with the highest comprehensive quality score to obtain the optimal code scheme.

[0261] Specifically, after generating the functional code, a series of advanced probability theories and combinatorial optimization techniques are used to precisely sample and select the optimal solution from the code generation space. This process delves into the probability space structure of code generation, enabling multi-dimensional evaluation and optimization of candidate solutions.

[0262] First, a code generation graph is constructed based on the functional code. This is a weighted directed graph representing the code generation space. The vertices of the graph represent different states in the code generation process, including partially generated code snippets and their corresponding program states (such as variable environments, type contexts, and control flow information). Edges in the graph represent transitions from one state to another, typically corresponding to the addition of a code marker or code block. Each edge carries a weight, representing the probability or quality score of the transition, determined by the language model and code quality metrics. The system implements state compression technology, merging semantically equivalent program states into a single vertex, significantly reducing the graph size and making subsequent analysis more efficient. Special attention is paid to decision point marking, identifying key decision points in the code generation graph. These points correspond to important choices in code implementation, such as algorithm strategies, data structure selection, or concurrency model determination.

[0263] We apply the two-forest representation theory to the weighted code generation graph, decomposing it into multiple spanning tree forests. A two-forest is a concept in graph theory, referring to a graph that can be decomposed into two disjoint forests, where a forest is a cycle-free graph composed of several trees. We implement a two-forest decomposition algorithm, which first identifies key decision points in the graph and sets them as the root nodes of the trees. Then, using breadth-first search, we construct the corresponding spanning tree starting from each root node until we encounter another decision point or reach a termination state. We use an edge weight aggregation function to combine the edge weights in the spanning trees into an overall quality score for the tree, reflecting the overall quality of the code logic fragment. We pay special attention to tree boundary optimization, adjusting the tree partition boundaries so that each tree corresponds to a semantically complete code logic unit, such as a function, a control structure block, or a type definition. Finally, the code generation graph is decomposed into multiple two-forests, each containing several unconnected trees, and each tree representing a cohesive code generation logic fragment.

[0264] Based on two-forest sets, a two-forest sampling algorithm based on Markov Chain Monte Carlo (MCMC) is designed. MCMC is a class of algorithms that sample by constructing Markov chains, suitable for complex probability distributions. A transition kernel function between two forests is defined, which describes the probabilistic mechanism of transitioning from one two-forest state to another. The transition kernel function typically includes three basic operations: adding / deleting an edge, swapping two edges, or redistributing the root node of the tree. The Metropolis-Hastings algorithm is implemented, which determines whether to accept a new state by calculating the probability ratio between the proposed state and the current state, ensuring that the sampling process eventually converges to the target probability distribution. A temperature parameter is introduced to adjust the calculation of the acceptance probability; at high temperatures, the system tends to explore more, while at low temperatures, it tends to develop known high-quality solutions. A state random walk is performed, starting from the initial two-forest, and continuously transitioning to new two-forest states according to the transition kernel function and acceptance probability, forming a Markov chain. After a sufficient burn period, samples are collected, ultimately sampling N representative two-forests, each corresponding to one possible code implementation scheme.

[0265] For a sampled binary forest set, the Deterministic Point Process (DPP) technique is applied to select the most representative and diverse subset of samples. DPP is a probabilistic model, particularly suitable for modeling the diversity of elements in a set. First, the similarity matrix between samples in the sampled binary forest set is calculated, and the binary forest edit distance is used to measure the structural differences between the two forests. A kernel matrix is ​​constructed, converting the similarity matrix into a DPP kernel matrix, where each element represents the similarity or quality of the corresponding sample pair. The k-DPP algorithm is applied, which ensures diversity while considering sample quality by maximizing the determinant of the sample set. Determinant maximization essentially involves selecting a set of mutually orthogonal vectors, ensuring that the selected samples are significantly different from each other. A greedy subset selection strategy is implemented, selecting samples that maximize the increase in the determinant of the current subset each time, until k samples are selected. The final diverse candidate solution set contains k high-quality and significantly different code implementations.

[0266] Each code implementation from the diverse candidate solution set is evaluated in depth. First, a Markov chain transition matrix corresponding to the code is constructed, describing the transition probabilities between different states during program execution. The system calculates the stationary distribution of this matrix, reflecting the probability of the program being in each state during long-term execution; it also calculates the average first-arrival time matrix, representing the average number of steps to reach another state from one state. Based on the stationary distribution and the average first-arrival time matrix, the Kemeney constant is calculated. This is an indicator of the Markov chain mixing speed, reflecting the program's execution efficiency and process complexity. A smaller Kemeney constant indicates more efficient state transitions and a more optimized code execution path.

[0267] Static analysis tools are used to analyze the time and space complexity of each code implementation. A system resource usage analyzer is implemented, which identifies loop structures, recursive calls, and data structure operations in the code, estimating the worst-case, average-case, and best-case time complexity. Scala-specific performance considerations, such as closure creation overhead, immutable collection operations, and implicit conversions, are taken into account to accurately assess the performance impact of these language features. Space complexity is also analyzed, considering factors such as object creation, collection size, and recursion stack depth. The analysis results form a code performance prediction score, which comprehensively reflects the efficiency and resource usage characteristics of the code implementation.

[0268] The structural dissimilarity among code implementation schemes in the diverse candidate scheme set is also calculated. Using abstract syntax tree edit distance, the minimum number of edit operations required to transform the AST of one code scheme into the AST of another is calculated. A weighted edit distance algorithm is implemented, assigning different weights to different types of edit operations (adding, deleting, modifying nodes) to reflect the semantic importance of the operations. A structural dissimilarity matrix is ​​constructed, and the average dissimilarity of each scheme to all other schemes is calculated to obtain a diversity index score. A high diversity index score indicates that the scheme has a unique implementation strategy, providing diverse options for the final selection.

[0269] Finally, based on the Kemoni constant score, code performance prediction score, and diversity index score, a comprehensive quality score is calculated for each solution according to preset weighting coefficients. This achieves multi-objective weighted summation, combining the three indicators according to their importance to the task requirements. Adaptive weight adjustment is provided, dynamically adjusting the weight configuration based on project type and performance requirements; for example, performance score weight is increased for performance-critical applications, and diversity index weight is increased for innovative projects. Ultimately, the code implementation solution with the highest comprehensive quality score is selected as the optimal code solution, achieving the best balance in execution efficiency, resource utilization, and implementation strategy uniqueness.

[0270] This method, based on advanced probability theory and combinatorial optimization, can accurately sample and select the optimal solution from the code generation space, greatly improving the quality and diversity of generated code and providing users with efficient and innovative code implementations.

[0271] Step S4: Perform code formatting, type checking, performance optimization, and testing on the optimal code scheme, automatically generate documentation comments, and obtain the final, complete code.

[0272] In one embodiment of the present invention, the step of performing code formatting, type checking, performance optimization, and testing on the optimal code scheme, and automatically generating documentation comments to obtain the final, complete code, includes: extracting indentation style, line break rules, naming conventions, and space usage rules based on the project's unique style fingerprint, constructing formatting configuration parameters, and obtaining a project style configuration; formatting the optimal code scheme using the Scalafmt tool according to the project style configuration, unifying the formatting elements including indentation, line breaks, and spaces, and obtaining formatted code; calling the Scala compiler's type checking function to perform static type analysis on the formatted code to verify type safety, outputting a type check report containing the location of type errors, error types, and suggested correction schemes, and correcting the detected type errors or type errors according to the suggested correction schemes in the type check report. The code is corrected for mismatches to obtain type-safe code. Static analysis tools are used to evaluate the performance of this type-safe code, identifying potential performance bottlenecks such as redundant traversals that repeatedly perform the same calculations within a single loop, repeated evaluations of the same expression, and boxing / unboxing operations involving frequent conversions between value types and reference types. Performance optimization patterns are then applied to refactor the code, resulting in performance-optimized code. Based on the input / output types and functional descriptions of the performance-optimized code, unit test cases covering normal inputs, boundary conditions, and exceptional situations are automatically generated. These tests are then executed to verify the code's functionality, resulting in verified code. Finally, the structure and functional implementation of the verified code are analyzed, and documentation comments conforming to the Scaladoc specification are automatically generated. These comments include method descriptions, parameter explanations, return value explanations, and usage examples, resulting in the final, complete code.

[0273] Specifically, the optimal code solution undergoes comprehensive refinement. Through formatting, type checking, performance optimization, and testing, it is ensured that the generated code not only functions correctly but also conforms to the project's quality standards and style guidelines. This process covers all dimensions of code quality assurance, enabling the generated code to be seamlessly integrated into existing projects.

[0274] First, based on the project's unique style fingerprint, formatting configuration parameters are extracted and constructed. A style fingerprint is a feature extraction process that extracts inherent coding style characteristics from the project's codebase. It identifies the project's unique formatting preferences and coding conventions by analyzing existing code. A style feature extractor is implemented, which scans the project's source code files and statistically analyzes four core formatting features: indentation style (e.g., use of spaces or tabs, number of spaces per indentation level), line break rules (e.g., curly brace placement, line break methods in method chaining), naming conventions (e.g., camelCase or underscore naming, naming patterns for different types of identifiers), and space usage rules (e.g., space usage before and after operators, space usage in parameter lists and within curly braces). Frequency analysis is then used to identify the most common patterns for each formatting feature, determining the project's mainstream style.

[0275] It also implements style consistency assessment to evaluate the level of style consistency across projects. For projects with high style consistency, the mainstream styles are directly used; for projects with inconsistent styles, style clustering analysis is performed, grouping code according to style features, identifying multiple possible style standards, and selecting the style closest to the module containing the optimal code solution. Based on the extracted style features, the system constructs the formatted configuration parameters required by the Scalafmt tool, including parameters such as indentation size, alignment strategy, bracket line break rules, and maximum line width, forming a complete configuration file that conforms to the project style.

[0276] Next, the optimal code solution is formatted using the Scalafmt tool. Scalafmt is a code formatting tool in the Scala ecosystem that can reformat code based on configuration rules to conform to specified formatting standards. First, a pre-formatting compatibility check is performed to verify that the code contains any syntax features that might cause formatting problems, such as special comments or complex multi-line expressions. Then, a progressive formatting strategy is implemented, breaking the code down into multiple independent blocks (such as method and class definitions) and applying formatting to each block, avoiding formatting failures for complex code structures.

[0277] Special attention was paid to Scala-specific formatting challenges, such as implicit parameter blocks, pattern-matching indentation, and type parameter formatting. Formatting result validation was implemented by comparing the Abstract Syntax Tree (AST) before and after formatting to ensure that the formatting process did not alter the semantics of the code. For formatting that might cause ambiguity (such as a few expressions where changes in whitespace could lead to different parsing), necessary parentheses or explicit type annotations were added to eliminate potential ambiguity. After formatting, the resulting code conformed to project specifications in terms of indentation, line breaks, and whitespace.

[0278] Then, the Scala compiler's type checking functionality is invoked to perform static type analysis on the formatted code. Scala boasts a powerful type system with complex features such as parametric polymorphism, higher-order types, path-dependent types, and implicit conversions, which are crucial for type safety verification. Enhanced type checking is implemented, enabling stricter checking options beyond basic type consistency checks, such as strict type inference, detailed implicit resolution tracing, and deprecated API warnings. Detailed type checking results are collected through the compiler API, including successfully typed expressions and their inferred types, the location and nature of type errors, and a detailed process of implicit resolution.

[0279] Implement a type error classifier to categorize type errors according to their nature, such as type mismatch, method not found, missing implicit parameters, or type inference failure. For each type of error, implement a dedicated error correction generator to generate possible correction schemes based on the error type and context. For example, for type mismatch errors, the system analyzes the structure of the source and target types, considering possible conversion functions or implicit conversions; for method not found errors, it searches for methods with the same name or similar semantics and provides alternative call suggestions. Special attention is paid to Scala-specific type errors, such as implicit parameter resolution conflicts or path-dependent type issues, providing targeted correction schemes.

[0280] Based on the suggested corrections in the type check report, necessary corrections are automatically applied. A safe correction applicator is implemented; this component performs impact analysis before applying corrections to ensure that the corrections do not introduce new type errors or change the original semantics of the code. A progressive correction strategy is adopted, resolving simple and clear type errors first, then handling complex errors that may have multiple correction options. For complex type errors for which the optimal correction cannot be automatically determined, the principle of minimum invasiveness is implemented, selecting the correction option with the least modification to the code structure. After corrections are completed, a full type check is performed again to ensure that all type issues have been resolved, resulting in type-safe code.

[0281] Static analysis tools are used to perform performance evaluations on type-safe code, identifying potential performance bottlenecks. A Scala performance pattern detector is implemented to specifically identify common performance issues in Scala code. This detector focuses on three main types of performance bottlenecks:

[0282] 1. Redundant traversal refers to repeatedly performing the same collection traversal operation within a single loop. The system identifies collection operations within the loop through data flow analysis, detecting whether there are multiple traversals that can be merged, such as merging the filter followed by map operation into a single collect operation.

[0283] 2. Redundant evaluation refers to evaluating the same expression multiple times within an invariant context. Expression equivalence analysis identifies semantically equivalent but repeatedly evaluated expressions in the code, paying particular attention to computationally expensive function calls or complex expressions.

[0284] 3. Boxing and unboxing operations refer to frequent conversions between value types (such as Int) and reference types (such as Integer). Type flow analysis traces the propagation path of value types in the code and identifies operational patterns that may lead to implicit boxing and unboxing, such as scenarios where a collection of primitive types is converted to a functional operation and then back to a primitive type.

[0285] A series of performance optimization patterns were applied to refactor the code. For redundant traversal, a merged collection operation pattern was used to combine multiple traversals into a single one; for repetitive calculations, a result caching pattern was applied to store the results in local variables for reuse; for boxing and unboxing operations, a dedicated type API pattern was applied, using dedicated methods optimized for primitive types. Special attention was paid to Scala-specific performance optimization opportunities, such as using `.view` for deferred collection operations, using `@specialized` annotations to avoid generic boxing, and optimizing closure capture to reduce object creation. Refactoring safety verification was implemented to ensure that performance optimizations do not change the original semantics of the code, and the optimization effects were evaluated through benchmark testing.

[0286] For the verified code, unit test cases are automatically generated to verify functional correctness. An input space analyzer is implemented to divide the input space into different equivalence classes, such as normal value ranges, boundary values, and outliers, based on the method's parameter types and constraints. Representative test cases are generated for each equivalence class, and test cases for specific logical branches in the code are added to ensure comprehensive test coverage. Special attention is paid to exception handling and boundary condition testing, generating inputs that may trigger exceptions or boundary behaviors to verify code robustness. Expected result verification is implemented, defining explicit expected outputs or behaviors for each test case to form complete test assertions. By executing the generated test cases, the correctness of the code under various input conditions is verified, and test coverage data is collected to ensure that critical code paths are tested.

[0287] Finally, the verified code is analyzed, and documentation comments conforming to the Scaladoc specification are automatically generated. A code intent analyzer is implemented to infer the functional intent and design ideas of the code by analyzing its structure, naming patterns, and control flow. The generated documentation comments include four key parts: method description (an overview of the method's main function and purpose), parameter description (detailing the meaning, type, and constraints of each parameter), return value explanation (explaining the meaning and possible range of return values), and usage examples (providing concise code examples demonstrating how to use the method). Special attention is paid to the documentation of exceptional cases and special conditions, clearly specifying the types of exceptions that may be thrown and their triggering conditions. Documentation consistency checks are also implemented to ensure that the documentation descriptions are consistent with the actual code behavior, and that parameter names and type descriptions are accurate.

[0288] After this series of optimization and refinement steps, the final, perfected code was obtained. This code is not only functionally correct and type-safe, but also conforms to the project's style guidelines, possesses good performance characteristics, and has complete documentation. This comprehensive code refinement process ensures the high quality and maintainability of the generated code, making it directly usable in a production environment and easy for other developers to understand and extend.

[0289] In one embodiment of the present invention, the performance evaluation of the type-safe code using static analysis tools identifies potential performance bottlenecks, including redundant traversal of repeatedly performing the same calculation within a single loop, repeated calculation of evaluating the same expression multiple times, and boxing / unboxing operations involving frequent conversions between value types and reference types. Performance optimization patterns are then applied to refactor the code to obtain performance-optimized code. This includes: constructing a dynamic evolution graph of the project code based on historical project versions or simulating future expansion scenarios. Nodes in the dynamic evolution graph represent the states of code elements at different points in time, edges represent dependencies and their evolution between elements, and the time dimension records the temporal information of state changes, resulting in a time-varying code evolution graph; calculating the corresponding Kemoni constant for each time slice of the time-varying code evolution graph; analyzing the time-series characteristics and trends of the Kemoni constant to calculate a fitness index based on the Kemoni constant and its corresponding rate of change, resulting in a fitness index sequence; and calculating the fitness index between adjacent time slices based on the fitness index sequence. The standard deviation of the standard deviation is used to identify substructures with standard deviations exceeding a preset fluctuation threshold as vulnerable points with large fluctuations in the Caimeni index. The degree centrality of each node in the time-varying code evolution graph is calculated, and nodes with the top 20% degree centrality and whose edge connection number changes more than a preset connection stability threshold in adjacent time slices are identified as vulnerable points with high centrality but unstable connections. Time-related dependency change patterns are analyzed to identify potential vulnerabilities, resulting in a code vulnerability set. For the code vulnerability set, a forward-looking optimization strategy is applied, which enhances the abstraction layer and interface design, introduces the adapter pattern to reduce direct dependencies, applies the strategy pattern to enhance scalability, and optimizes resource management and concurrency control mechanisms to obtain forward-looking optimized code. Based on the optimization results of the forward-looking optimized code, a code adaptability analysis report is generated, which includes potential risk points and their evolution sensitivity, applied forward-looking optimization measures, long-term maintenance suggestions, and a quantitative evaluation of adaptability indicators. The code adaptability analysis report is used as supplementary content to the code documentation to obtain the performance-optimized code.

[0290] Specifically, implementing a forward-looking performance optimization strategy not only focuses on the current performance of the code but also considers its adaptability and stability during project evolution. This optimization approach is based on in-depth analysis of how the code structure changes over time, identifying potential vulnerabilities and applying targeted optimization strategies to ensure that the code maintains high performance and maintainability throughout the long-term development of the project.

[0291] First, a dynamic evolution graph of the project code is constructed based on historical versions of the project or simulated future expansion scenarios. A dynamic evolution graph is a multi-dimensional graph structure used to represent the changes of code elements and their dependencies over time. Unlike traditional static dependency graphs, dynamic evolution graphs add a time dimension, enabling them to capture the evolutionary patterns of the code structure. A historical version analyzer is implemented, which extracts historical versions of the project from the version control system and analyzes the state and dependencies of code elements (such as classes, methods, and variables) in each version. For new projects or situations where historical data is insufficient, evolutionary scenario simulations are implemented, simulating possible future change paths based on common evolutionary patterns in software engineering and the characteristics of the current project.

[0292] When constructing the dynamic evolution graph, multi-level element tracking was implemented, simultaneously tracking code elements at different granularities: file level (including changes in relationships), type level (changes in inheritance and implementation relationships), method level (changes in call relationships), and expression level (changes in data flow). For each element, multiple attributes at different points in time were recorded: structural attributes (such as method signatures and class hierarchies), semantic attributes (such as functional descriptions and comments), and metric attributes (such as complexity and number of lines). Dependency change analysis was also implemented, tracking the creation, modification, and deletion of dependencies between elements to form a complete dependency evolution history.

[0293] Special attention is paid to identifying change hotspots, specifically code regions that change frequently throughout historical versions. These regions typically represent unstable or continuously evolving features. Collaborative change pattern extraction is implemented, identifying groups of code elements that are frequently modified together; these elements may have unspoken hidden dependencies. Combining this information, a time-varying code evolution graph is constructed. The nodes of this graph represent the state of code elements at different points in time, the edges represent the dependencies between elements and their evolution, and the time dimension records the temporal information of state changes.

[0294] The Kemoni constant is calculated for each time slice of the time-varying code evolution graph. The Kemoni constant is an index in Markov chain theory used to measure the average number of steps required for a stochastic process to reach a stationary distribution from any initial state. In code analysis, the Kemoni constant can be used to quantify the complexity of the code structure and the efficiency of state transitions. The code execution flow is modeled as a Markov process, with states corresponding to control points in the code, and transition probabilities based on control flow and call relationships. The code transition matrix is ​​constructed, converting the code's control flow graph into a state transition matrix, representing the transition probability from one control point to another.

[0295] Matrix eigenvalue analysis is performed to calculate the eigenvalues ​​and eigenvectors of the transition matrix, determining the basic properties of the Markov chain, such as stationary distribution and mixed-time characteristics. The Kemoni constant is calculated based on the matrix eigenvalues; a low constant indicates a simple and efficient code execution flow, while a high constant indicates a complex execution path or suboptimal state transitions. This calculation process is repeated for each time slice in the evolution graph to obtain a time series of the Kemoni constant. A rate of change analyzer is implemented to calculate the rate of change of the Kemoni constant between adjacent time points, reflecting the speed of code complexity evolution. The system combines the absolute value of the Kemoni constant and its rate of change to calculate the fitness index, a comprehensive indicator reflecting the code structure's ability to adapt to changes while maintaining performance. A high fitness index indicates that the code has maintained good structural and performance characteristics during evolution, while a low index indicates poor adaptability to changes. The fitness index is calculated for each time slice, forming a complete fitness index sequence.

[0296] Based on the fitness index sequence, vulnerabilities in the code are identified. First, the standard deviation of the fitness index between adjacent time slices is calculated, reflecting the degree of fluctuation in code structure stability. Substructures with standard deviations exceeding a preset fluctuation threshold are identified; these regions exhibit significant instability during project evolution and are marked as vulnerabilities with large Kemoni index fluctuations. Degree centrality analysis is performed, calculating the degree centrality of each node in the time-varying code evolution graph, reflecting the degree of connection between that node and other nodes. Nodes with high degree centrality are usually key components in the system and have a significant impact on the overall structure. Nodes ranking in the top 20% of degree centrality and whose edge connection number changes exceeding a preset connection stability threshold in adjacent time slices are identified; these nodes are high-centrality but unstable vulnerabilities, and their changes will have a wide-ranging impact on the system.

[0297] Time-dependent dependency analysis was also conducted to study the changing patterns of code dependencies over time. Several unstable dependency patterns were identified: rapidly growing dependencies (a sharp increase in the number of dependencies within a short period), oscillating dependencies (frequent establishment and dissolution of dependencies), and shifting dependencies (dependencies gradually shifting from one set of components to another). These unstable dependency patterns typically indicate areas of unclear design or blurred responsibility boundaries, representing potential vulnerabilities. By combining these analyses, a complete set of code vulnerabilities was identified—areas in the code that are particularly sensitive to changes or may become future performance bottlenecks.

[0298] For the identified set of code vulnerabilities, a proactive optimization strategy is applied. An abstraction layer strengthener is implemented to reduce direct dependencies between components by introducing or improving abstract interfaces. The dependency characteristics of vulnerabilities are analyzed, and appropriate abstract interfaces are designed to encapsulate potentially changing implementation details and provide stable interaction contracts. The system pays special attention to Scala's trait mechanism, leveraging its flexible composability to design reusable component interfaces. Interface stability enhancements are implemented by expanding the capabilities of existing interfaces to reduce future interface changes due to feature expansion.

[0299] The Adapter pattern is introduced to add an adaptation layer between components with frequently changing dependencies. The Adapter pattern allows classes with incompatible interfaces to work together, making it an effective strategy for handling changing components. Analyze the dependency direction and nature of vulnerabilities, design a dedicated adapter that translates interface requests, isolates changes, and provides additional functionality such as caching or retries when necessary. The Strategy pattern is applied to enhance this by encapsulating potentially changing algorithms into independent strategy classes. The Strategy pattern allows algorithms to vary independently of the clients that use them, making it particularly suitable for handling vulnerabilities where algorithm logic changes frequently. Design strategy interfaces and implementation families, and provide a strategy selection mechanism so that the system can dynamically adapt to different algorithm requirements.

[0300] Optimize resource management and concurrency control mechanisms to ensure performance stability under high load and complex concurrency scenarios. The system implements a resource pooling strategy, pooling frequently created and destroyed resources to reduce resource allocation overhead. A fine-grained locking mechanism is implemented to replace identified coarse-grained locking, improving concurrency performance. The system pays special attention to Scala's concurrency primitives, such as Future, Promise, and Actor, optimizing their usage patterns to ensure efficient asynchronous processing and message passing. A backpressure handling strategy is implemented, adding flow control mechanisms to components handling data streams to prevent system crashes during load fluctuations. These optimizations collectively improve code scalability and performance stability, enabling it to adapt to future demand growth.

[0301] Based on the results of the comprehensive forward-looking optimizations, a detailed code adaptability analysis report is generated. The report first lists the identified potential risks and their evolutionary sensitivities, providing a detailed description of each risk, including its location in the code, the nature of the risk, and its potential impact on system stability. The report documents the applied forward-looking optimization measures, detailing the purpose, implementation method, and expected results of each measure. Finally, the report provides long-term maintenance recommendations, offering suggestions for continuous monitoring and maintenance for different types of vulnerabilities, such as regular code reviews, performance testing, or refactoring plans.

[0302] The report also includes a quantitative assessment of adaptability metrics, using data visualization techniques to display historical trends and predicted directions for the adaptability index. It provides benchmark analysis, comparing the performance of optimized code with the original code in multiple scenarios to quantify the optimization effect. Furthermore, it offers scalability limit assessment, evaluating the code's maximum processing capacity and scalability bottlenecks by simulating extreme load scenarios. These quantitative metrics enable developers to objectively evaluate the optimization effect and remaining risks.

[0303] The code adaptability analysis report is submitted to the version control system along with the code, supplementing the code documentation. The report's format is designed for easy understanding and reference by team members, including interactive charts, code examples, and explanations of the decision-making process. The report not only documents current optimization decisions but also provides guidance for future maintenance and evolution, becoming an important part of the project's knowledge base.

[0304] This forward-looking optimization approach based on time-varying code evolution graphs not only solves current performance issues but also prevents potential future performance degradation and maintenance difficulties. The optimized code not only boasts excellent performance but also exhibits greater adaptability and scalability, better supporting the long-term development of projects. This method is particularly suitable for long-term maintenance-critical systems, significantly reducing technical debt and maintenance costs, and improving the overall quality and lifecycle value of the software.

[0305] In one embodiment of the present invention, a comprehensive performance evaluation of type-safe code is conducted using static analysis tools to accurately identify various performance bottlenecks and implement targeted refactoring and optimization. This can significantly improve execution efficiency and resource utilization while ensuring that the code functionality remains unchanged. The process is as follows:

[0306] First, static analysis is used to pinpoint performance bottlenecks. An Abstract Syntax Tree (AST) is constructed to fully preserve the syntactic structure and semantic information. Traversing the AST identifies set operations, computational expressions, and type conversions, providing foundational data for performance analysis. Next, a Control Flow Graph (CFG) is built, breaking down the code into sequentially executed basic blocks. Edge connections represent the execution order, accurately identifying loops, conditional branches, function calls, and frequently executed, computationally intensive hotspots. The execution complexity of each basic block is calculated, pinpointing high-complexity performance bottleneck areas. Simultaneously, data flow analysis is performed, tracing variable definition-usage chains to analyze variable lifecycles and value propagation paths, focusing on identifying repetitive calculations, especially invariant expressions within loops, which are the core optimization entry points.

[0307] Based on this, we conducted specialized performance analysis, focusing on collection operation patterns, and eliminating unnecessary multiple traversals, repeated accesses to the same collection in nested loops, and redundant operations such as repeated conversions of collections, to avoid quadratic and higher complexity overhead. We analyzed type usage patterns, identified excessive boxing and unboxing operations in loops and frequently called methods, which create temporary objects and increase garbage collection pressure. We also analyzed memory usage and allocation patterns, detected excessive creation of temporary objects in loops and hot methods, identified reusable and pooled object scenarios, analyzed the lifecycle of large objects, and avoided excessive memory consumption.

[0308] After completing the full-dimensional analysis, a detailed list of performance bottlenecks is generated, sorted by the degree of performance impact. Each entry clearly defines the location, type, impact level, and optimization suggestions, thus defining clear priorities and optimization directions for subsequent code refactoring.

[0309] Based on the bottleneck list, the code was refactored using a systematic application performance optimization pattern. Collection operations were optimized by merging multiple iterations into a single iteration, refactoring nested loops into single-level loops, and replacing them with efficient data structures like HashMap to reduce lookup overhead. Lazy evaluation was introduced to replace eager evaluation; List was replaced with Stream or LazyList; view collections were used to avoid creating intermediate collections; and incremental computation was used to update only necessary data, reducing unnecessary calculations. A caching mechanism was introduced to eliminate redundant calculations; memoized caching was implemented for pure functions; loop invariants were moved outside the loop for reuse; and the results of high-overhead expressions were stored in local variables for repeated calls.

[0310] Simultaneously, optimize type usage by replacing wrapper types with primitive types, using dedicated primitive type collections to reduce boxing and unboxing, and avoiding type erasure and boxing overhead in generic code through type specialization; implement memory optimization by replacing string concatenation with StringBuilder, reusing frequently used small objects through object pooling, and setting reasonable initial capacities for collections to avoid memory copying during dynamic expansion; promote concurrency optimization by enabling parallel collections for computationally intensive operations, optimizing lock granularity, using read-write locks or lock-free data structures to reduce thread contention, and asynchronousizing IO-intensive operations to improve multi-core utilization and system throughput.

[0311] After each optimization is completed, incremental compilation and functional verification are immediately performed to ensure that the optimization does not compromise code correctness. Continuous iterative optimization continues until the bottleneck is effectively resolved or the optimization benefits outweigh the costs. The final performance-optimized code fully retains the original functional semantics while significantly improving execution efficiency and reducing resource consumption, achieving a highly efficient balance between performance and correctness.

[0312] In one embodiment of the present invention, to ensure the functional correctness of the optimized code, the system automatically generates a comprehensive test case set through a multi-dimensional strategy to achieve full-scenario coverage verification. The core process is as follows:

[0313] First, input / output analysis is conducted as the foundation for test case generation. Method signatures are accurately extracted, and the basic, collection, and custom types of parameters and return values ​​are identified. Simultaneously, non-nullable and range restrictions in annotations, as well as conditional checking rules within the code, are parsed to clarify valid input boundaries and constraints, thus defining the core scope for subsequent test case design.

[0314] Test cases for normal scenarios are generated based on input characteristics to verify the functional effectiveness of the code in typical use cases. For numeric, string, set, and custom types, uniformly distributed samples, strings of different lengths / character combinations, sets of varying sizes, and complete custom objects are generated respectively. Equivalence class partitioning is used to select representative samples from each equivalence class, reducing the number of test cases while ensuring coverage. For multi-parameter methods, pairwise / orthogonal combination testing is employed to cover key parameter combinations. Simultaneously, the code execution path is analyzed, and dedicated test cases are designed for conditional branches, loops, and exception handling blocks, focusing on boundary values ​​of conditional expressions while also considering implicit conditional branches such as type conversion and null checks, ensuring coverage of all major execution paths.

[0315] Next, boundary condition test cases are generated to verify the code's ability to handle extreme scenarios. Boundary scenarios for various inputs are analyzed: for numeric types, tests are conducted on maximum / minimum values, zero, and critical overflow values; for set types, tests are conducted on empty sets, single-element sets, sets with duplicate elements, and very large sets; for strings, tests are conducted on empty strings, single strings, and special strings. Emphasis is placed on covering explicit boundary conditions handled by the code, while also investigating implicit boundary risks such as division by zero and array out-of-bounds errors. Valid and invalid input transition points are accurately identified, and test data for critical values ​​and ±1 of the critical values ​​are generated. Verification is strengthened for boundary transition points with high error rates to ensure the robustness of the code under extreme scenarios.

[0316] Subsequently, test cases for exceptional conditions are generated to verify the code's fault tolerance capability for unexpected input. Test scenarios such as null references, type mismatches, and format errors are designed, with dedicated test cases for declared exceptions, try-catch blocks, and throw statements, while also covering undeclared runtime exceptions such as null pointer exceptions, array out-of-bounds exceptions, and type conversions. The focus is on testing resource release, state recovery, and error message feedback capabilities under exceptional scenarios, comprehensively verifying the code's error handling mechanism and building a robust defense.

[0317] After test case generation is complete, they are integrated into a complete test suite and executed automatically: setting up the test environment, executing the code under test, capturing output and exceptions, comparing the results with expected results to determine whether the test cases pass, marking failed test cases and analyzing the reasons. Simultaneously, coverage metrics such as statements, branches, conditions, and paths are evaluated, and test cases are added to uncovered areas to ensure that each module of the code is fully validated. The core verification checks the functional equivalence between the optimized code and the original code, focusing on verifying the consistency of boundary conditions and exception handling behavior to ensure that performance optimization does not change the core semantics.

[0318] For failed test cases, defect localization analysis is conducted. By comparing input characteristics, execution paths, and optimization changes, the causes of failures are accurately identified, and remedial suggestions are provided. A complete report is generated, including coverage, pass rate, and failed test case analysis. If all tests pass, the code is confirmed to be delivered in compliance with regulations; if issues are found, developers are guided to adjust and optimize, achieving a balance between performance improvement and functional correctness, and providing reliable quality assurance for the project.

[0319] like Figure 2 As shown, the present invention also provides an intelligent Scala code generation system based on artificial intelligence, comprising:

[0320] The requirement parsing module 10 is used to obtain the natural language requirement description input by the user, perform semantic understanding and information extraction on the natural language requirement description, extract functional description, input and output type and constraint conditions, obtain a standardized set of technical elements, and map the standardized set of technical elements to the syntax structure and programming paradigm of the Scala language to obtain the requirement semantic vector.

[0321] The context-aware module 20 is used to perform static analysis on the project codebase, extract the project code structure, dependencies and coding style, construct a multi-level context knowledge graph, and use graph neural networks and attention mechanisms to process the multi-level context knowledge graph to obtain a context vector representation.

[0322] The code generation engine 30 is used to fuse the requirement semantic vector and the context vector representation to obtain a joint vector representation, and to generate a code framework and core code based on the joint vector representation using a pre-trained Scala code generation model. It also generates multiple candidate code implementation schemes through a beam search algorithm and selects the optimal scheme to obtain the optimal code scheme.

[0323] The code optimization and verification module 40 is used to perform code formatting, type checking, performance optimization and test verification on the optimal code scheme, automatically generate documentation comments, and obtain the final and improved code.

[0324] It also includes five core functional modules: user interaction module, requirement analysis module, context awareness module, code generation engine, code optimization and verification module, and user interaction module. These modules communicate through a unified interface to achieve overall collaborative work.

[0325] In practical applications, the user interaction module is integrated into the development environment as an IDE plugin, providing an intuitive user interface, supporting real-time code suggestions and user feedback, and collecting user evaluations and modification information on the generated code for continuous system learning and improvement.

[0326] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent Scala code generation method based on artificial intelligence, characterized in that, include: Obtain the user's input natural language requirement description, perform semantic understanding and information extraction on the natural language requirement description, extract functional description, input and output types and constraints, obtain a standardized set of technical elements, and map the standardized set of technical elements to the syntax structure and programming paradigm of the Scala language to obtain a requirement semantic vector; Static analysis is performed on the project codebase to extract the project code structure, dependencies and coding style, construct a multi-level context knowledge graph, and process the multi-level context knowledge graph using graph neural networks and attention mechanisms to obtain context vector representations; The requirement semantic vector and the context vector representation are fused to obtain a joint vector representation. Based on the joint vector representation, a pre-trained Scala code generation model is used to generate a code framework and core code. Multiple candidate code implementation schemes are generated through a beam search algorithm, and the optimal scheme is selected to obtain the optimal code scheme. The optimal code solution is then formatted, type-checked, performance-optimized, and tested. Documentation comments are automatically generated to obtain the final, complete code.

2. The method according to claim 1, characterized in that, The process involves obtaining a natural language requirement description from user input, performing semantic understanding and information extraction on the description, extracting functional descriptions, input / output types, and constraints to obtain a standardized set of technical elements, and mapping this set to the syntax and programming paradigm of the Scala language to obtain a requirement semantic vector, including: Obtain the user's input natural language requirement description, and perform text cleaning, word segmentation, part-of-speech tagging and named entity recognition on the natural language requirement description to obtain a standardized text representation; Based on the standardized text representation, semantic encoding is performed using a pre-trained BERT model, and the user's core development intent is identified through an intent classifier. At the same time, the subject, behavior, and modifier relationships in the requirements are extracted through dependency parsing to obtain a structured intent representation. Based on the structured intent representation, an information extraction model is used to extract three types of technical information from the structured intent representation: functional description, input / output type, and constraints, to obtain a standardized set of technical elements. Based on the functional descriptions and constraints in the standardized set of technical elements, the standardized set of technical elements is mapped to the specific syntax structure and programming paradigm of the Scala language to obtain the mapped Scala semantic representation. The mapped Scala semantic representation is vector-encoded using the CodeBERT code pre-trained model to obtain the requirement semantic vector. The BERT model is used to extract the structured intent in the natural language requirement, and the CodeBERT model is used to vector-encode the mapped Scala semantic representation. The combination of the BERT model and the CodeBERT model can realize cross-modal feature extraction from natural language to programming language semantics.

3. The method according to claim 2, characterized in that, Based on the structured intent representation, an information extraction model is used to extract three types of technical information—functional description, input / output type, and constraints—from the structured intent representation, resulting in a standardized set of technical elements, including: Based on the structured intent representation, a specially trained information extraction model is used to identify keywords describing functions, extract functional descriptions of ranking algorithms or data filtering, and obtain a set of functional descriptions. Based on the structured intent representation, the information extraction model is used to identify type-related semantic components, extract the correspondence between input types and output types, and obtain a set of input and output types. Based on the structured intent representation, the information extraction model is used to identify semantic components related to performance constraints and security constraints, extract thread safety requirements and time complexity limits, and obtain a set of constraints. The set of functional descriptions, the set of input / output types, and the set of constraints are structurally integrated to obtain the standardized set of technical elements.

4. The method according to claim 2, characterized in that, The standardized set of technical elements, based on its functional descriptions and constraints, maps these elements to specific syntax structures and programming paradigms of the Scala language. A CodeBERT pre-trained model is then used to vectorize the mapped Scala semantic representation, resulting in the required semantic vector, which includes: Based on the functional descriptions in the standardized set of technical elements, the corresponding specific syntax structures of the Scala language are identified, sorting intentions are mapped to Scala collection operation methods, asynchronous processing intentions are mapped to Future or Actor models, and data transformation intentions are mapped to map or flatMap operations to obtain the syntax structure mapping results. Based on the constraints in the standardized set of technical elements, the corresponding Scala programming paradigm is identified, and thread safety requirements are mapped to immutable data structures or synchronization mechanisms, while performance requirements are mapped to tail recursion optimization or parallel sets, thus obtaining the programming paradigm mapping result. The syntactic structure mapping result and the programming paradigm mapping result are integrated to form a complete Scala semantic representation, resulting in the integrated Scala semantic representation; Using the CodeBERT code pre-trained model, feature extraction and vector encoding are performed on the integrated Scala semantic representation to generate a 768-dimensional semantic vector, thus obtaining the required semantic vector.

5. The method according to claim 1, characterized in that, The process involves static analysis of the project codebase to extract code structure, dependencies, and coding style, constructing a multi-layered context knowledge graph, and processing this multi-layered context knowledge graph using graph neural networks and attention mechanisms to obtain context vector representations, including: Static code analysis tools are used to construct abstract syntax trees for the project codebase, extracting classes, traits, objects, and methods in the project and their corresponding relationships to obtain a structured representation of the project code; Parse the project's build configuration file, extract the external libraries that the project depends on and their corresponding version information, and analyze the usage patterns of these external libraries in the project to obtain the project dependency graph; Analyze the existing code of the project, extract coding style features and common programming patterns, and obtain the project's unique style fingerprint; The structured representation of the project code, the project dependency graph, and the project-specific style fingerprint are structurally integrated to construct a multi-layered contextual knowledge graph that includes a code structure layer, a dependency layer, and a style pattern layer. The multi-layered context knowledge graph is used to extract node features and pass messages, and the relevance of information at each level to the current task is calculated and weighted by an attention mechanism to obtain the context vector representation.

6. The method according to claim 5, characterized in that, The process involves using a graph neural network to extract node features and perform message passing on the multi-layered context knowledge graph, and then calculating the relevance of information at each level to the current task through an attention mechanism and performing weighted fusion to obtain the context vector representation, including: Based on the multi-level context knowledge graph, an input representation of a graph neural network is constructed. The nodes in the multi-level context knowledge graph are encoded as feature vectors, and the edges are encoded as adjacency matrices to obtain a graph-structured input. A graph neural network is used to perform multi-layer message passing and feature aggregation on the graph structured input. The representation of each node is updated by aggregating the features of neighboring nodes to obtain the node update representation. Based on the node update representation, the attention mechanism is used to calculate the attention weights of each layer of information in the code structure layer, dependency layer, and style pattern layer with the current task, and the layer weight distribution is obtained. Based on the hierarchical weight distribution, the node representations of each level of information are weighted and summed to highlight the contextual features related to the current task, thus obtaining the context vector representation.

7. The method according to claim 6, characterized in that, The process involves fusing the requirement semantic vector and the context vector representation to obtain a joint vector representation, and then using a pre-trained Scala code generation model to generate a code framework and core code based on this joint vector representation. Multiple candidate code implementation schemes are generated using a beam search algorithm, and the optimal scheme is selected to obtain the optimal code scheme. This includes: Based on the demand semantic vector and the context vector representation, the correlation between the demand semantic vector and the context vector representation is calculated through a cross-attention mechanism to obtain the correlation weight, and vector fusion is performed according to the correlation weight to obtain the joint vector representation; Based on the joint vector representation, a pre-trained Scala code generation model using the Transformer architecture is loaded and initialized. The pre-trained Scala code generation model is used to generate a code framework including method signatures, class definition structures and import statements according to the functional intent and input / output type information in the joint vector representation, thus obtaining the code skeleton. Based on the functional descriptions and constraints in the code skeleton and the joint vector representation, and combined with the syntax features and programming paradigms of the Scala language, the core code that implements the main function is generated, resulting in the functional code. The beam search algorithm is used to initialize K generation paths for the functional code and maintain multiple generation paths through iterative expansion and probability evaluation. K candidate code implementation schemes are generated according to the probability distribution of the language model, resulting in a set of candidate schemes. For each candidate code implementation scheme in the candidate scheme set, calculate the language model probability, type matching degree and performance score, and calculate the comprehensive score based on the preset weight coefficient. Select the scheme with the highest comprehensive score to obtain the optimal code scheme.

8. The method according to claim 7, characterized in that, Based on the joint vector representation, a pre-trained Scala code generation model using the Transformer architecture is loaded and initialized. The pre-trained Scala code generation model is then used to generate a code framework including method signatures, class definition structures, and import statements based on the functional intent and input / output type information in the joint vector representation, resulting in a code skeleton, including: Based on the joint vector representation, a pre-trained Scala code generation model using the Transformer architecture is loaded, and the model parameters of the pre-trained Scala code generation model are adjusted through supervised learning on the Scala codebase to complete the model initialization and obtain the initialized generation model. Extract functional intent information from the joint vector representation, and use the initialized generative model to generate method signatures or class definition structures to obtain the code structure framework; The input and output type information is extracted from the joint vector representation, and the parameter list and return type are determined using the initialized generative model based on the input and output type information to obtain the type-labeled method signature; Based on the dependency information in the joint vector representation, the necessary import statements are generated using the initialized generative model to obtain a set of import statements. The code skeleton is obtained by integrating the code structure framework, the method signature of the type annotation, and the set of import statements.

9. The method according to claim 7, characterized in that, The process employs a beam search algorithm to initialize K generation paths for the functional code and maintains multiple generation paths through iterative expansion and probability evaluation. Based on the probability distribution of the language model, K candidate code implementation schemes are generated, resulting in a candidate scheme set, including: Based on the aforementioned functional code, initialize the beam search algorithm, set the beam width to K, create K initial generation paths, and obtain the initial path set; For each generated path in the initial path set, the probability distribution of the next code tag is calculated using the pre-trained Scala code generation model to obtain the tag probability distribution; Based on the label probability distribution, several candidate labels with the highest probability are selected for each generated path to expand the path, resulting in an expanded path set. Calculate the cumulative probability of each path in the expanded path set, select the K paths with the highest cumulative probability as the current retained paths, and obtain the updated path set; Repeat the calculation of the labeled probability distribution, the path expansion based on the labeled probability distribution, and the path selection based on the cumulative probability until all retained paths generate complete code implementations or reach the maximum length limit, thus obtaining the candidate solution set.

10. The method according to claim 7, characterized in that, Before performing static analysis on the project codebase to extract the project code structure, dependencies, and coding style, constructing a multi-layered context knowledge graph, and processing the multi-layered context knowledge graph using graph neural networks and attention mechanisms to obtain the context vector representation, the process further includes: Based on the structured representation of the project codebase, a code dependency graph is constructed. The vertices of the code dependency graph represent classes, traits, objects, and methods, and the edges represent call, inheritance, and containment relationships. Each edge is assigned a weight that reflects the strength of the dependency, resulting in a weighted code dependency graph. A fixed-parameter algorithm is applied to the weighted code dependency graph. Tree decomposition technique is used to decompose the weighted code dependency graph into a tree structure to limit the range of nodes that need to be traversed. Dynamic programming is used to accelerate the solution of subproblems to find vertex multi-way cut sets. The vertex multi-way cut set is a set of key nodes that can divide the weighted code dependency graph into multiple unconnected components that do not contain preset terminal nodes after removal. Based on the vertex multi-way cut set, identify the connected subgraphs in the weighted code dependency graph that are not segmented by the vertex multi-way cut set, enumerate the combinations of the connected subgraphs, and evaluate the cohesion and complexity of each connected subgraph through information entropy and modularity index to obtain the set of uncut components. Based on the vertex multipath cut set and the set of uncut components, a component relationship model is established. The component relationship model includes the dependency relationship between components, the information flow of data transmitted between components, and the control flow of the execution process across components. The vertex multi-way cut set, the uncut component set, and the component relationship model are integrated into the multi-layered context knowledge graph, and a functional module layer and a key interface layer are added to obtain an enhanced context knowledge graph.

11. The method according to claim 10, characterized in that, Based on the code skeleton and the functional descriptions and constraints in the joint vector representation, and combined with the syntax features and programming paradigms of the Scala language, the core code implementing the main functionality is generated, resulting in functional code, including: Based on the functional module layer and key interface layer in the enhanced context knowledge graph, the functional requirements to be generated are decomposed into relatively independent sub-tasks. Each sub-task corresponds to an uncut component or cross-component function in the uncut component set, thus obtaining a set of sub-tasks. Identify the nodes that connect different uncut components in the vertex multi-way cut set, generate interface code corresponding to the nodes, the interface code defines the interaction contract between different functional modules, and obtain the interface code set; Based on the set of uncut components, for each uncut component in the set of uncut components, corresponding implementation code is generated according to the internal dependency structure and functional characteristics of the uncut component, resulting in a set of internal implementation code for the module. Based on the component relationship model and the interface code set, code for inter-module collaboration is generated. The code for inter-module collaboration includes dependency injection mechanism, event passing logic, asynchronous communication implementation and error propagation handling, thus obtaining inter-module collaboration code. The interface code set, the module internal implementation code set, and the inter-module collaboration code are assembled into a complete implementation. The type consistency and semantic consistency of cross-module references are checked to ensure type safety and semantic matching between different modules, thus obtaining the functional code.

12. The method according to claim 10, characterized in that, Based on the functional descriptions and constraints in the code skeleton and the joint vector representation, and combined with the syntax features and programming paradigms of the Scala language, the core code implementing the main function is generated. After obtaining the functional code, the process further includes: Based on the functional code, a code generation graph is constructed. The vertices of the code generation graph represent the code segments to be selected and the corresponding program states during the code generation process. The edges represent the transition paths of code generation, and the edge weights represent the probability or quality score of state transitions, resulting in a weighted code generation graph. Applying the two-forest representation theory to the weighted code generation graph, the weighted code generation graph is decomposed into multiple spanning tree forests. Each two-forest contains several unconnected tree structures. The root node of each tree represents a key code decision point and each tree corresponds to a cohesive code generation logic fragment, thus obtaining a set of two-forests. Based on the aforementioned two-forest set, a two-forest sampling algorithm based on Markov chain Monte Carlo is designed. The transition kernel function between the two forests is defined, and the Metropolis-Hastings algorithm is applied to calculate the acceptance probability of state transition. By randomly walking, N representative two forests are sampled from the code generation space to obtain the sampled two-forest set. Deterministic point processing technique is applied to the sampled binary forest set to calculate the similarity matrix between samples in the sampled binary forest set. By maximizing the determinant of the sample set to ensure diversity, the k-DPP algorithm is applied to select the k most representative samples from the sampled binary forest set to obtain a diverse set of candidate solutions. For each code implementation scheme in the diverse candidate scheme set, construct the Markov chain transition matrix corresponding to the code implementation scheme, calculate the stationary distribution and average first-arrival time matrix of the Markov chain transition matrix, and calculate the Kemoni constant based on the stationary distribution and the average first-arrival time matrix to obtain the Kemoni constant score. Based on the Kemoni constant score, static analysis tools are used to analyze the time and space complexity of each code implementation scheme in the diverse candidate scheme set to obtain a code performance prediction score. Calculate the abstract syntax tree edit distance between any two code implementation schemes in the diverse candidate scheme set to obtain the structural dissimilarity matrix, and calculate the average structural dissimilarity of each code implementation scheme with other schemes based on the structural dissimilarity matrix to obtain the diversity index score; Based on the Kemoni constant score, the code performance prediction score, and the diversity index score, a weighted sum is performed according to a preset weighting coefficient to calculate the comprehensive quality score of each code implementation scheme in the diverse candidate scheme set; The optimal code solution is obtained by selecting the code implementation scheme with the highest overall quality score.

13. The method according to claim 12, characterized in that, The optimal code solution is then formatted, type-checked, performance-optimized, and tested. Documentation comments are automatically generated to obtain the final, complete code, including: Based on the project's unique style fingerprint, indentation style, line break rules, naming conventions, and space usage rules are extracted to construct formatting configuration parameters and obtain the project style configuration. The optimal code scheme is formatted using the Scalafmt tool according to the project style configuration, unifying the formatting elements including indentation, line breaks, and spaces to obtain standardized code. The Scala compiler's type checking function is invoked to perform static type analysis on the code of the specified format to verify type safety. The output is a type check report containing the location and type of type error and suggested corrections. Based on the suggested corrections in the type check report, the detected type errors or type mismatches are corrected to obtain type-safe code. Static analysis tools were used to evaluate the performance of the type-safe code, identifying potential performance bottlenecks, including redundant traversals that repeatedly perform the same calculations within a single loop, repeated calculations that evaluate the same expression multiple times, and boxing / unboxing operations that frequently convert between value types and reference types. Performance optimization patterns were then applied to refactor the code, resulting in performance-optimized code. Based on the input / output types and functional descriptions of the performance-optimized code, unit test cases covering normal input, boundary conditions, and abnormal situations are automatically generated, and the functional correctness of the code is verified by executing the tests, resulting in the verified code. Analyze the structure and functionality of the verified code, automatically generate documentation comments that conform to the Scaladoc specification, including method descriptions, parameter descriptions, return value explanations, and usage examples, to obtain the final, improved code.

14. The method according to claim 13, characterized in that, The static analysis tool is used to perform a performance evaluation on the type-safe code, identifying potential performance bottlenecks including redundant traversal that repeatedly performs the same calculation within a single loop, repeated calculations that evaluate the same expression multiple times, and boxing / unboxing operations that frequently convert between value types and reference types. Performance optimization patterns are then applied to refactor the code, resulting in performance-optimized code, including: Based on historical versions of the project or simulated future expansion scenarios, a dynamic evolution graph of the project code is constructed. The nodes of the dynamic evolution graph of the project code represent the state of code elements at different points in time, the edges represent the dependencies between elements and their evolution, and the time dimension records the temporal information of state changes, thus obtaining a time-varying code evolution graph. For each time slice of the time-varying code evolution graph, the corresponding Caimony constant is calculated. By analyzing the time series characteristics and changing trends of the Caimony constant, the fitness index based on the Caimony constant and its corresponding rate of change is calculated to obtain the fitness index sequence. Based on the adaptive index sequence, the standard deviation of the adaptive index between adjacent time slices is calculated. Substructures with standard deviations exceeding a preset fluctuation threshold are identified as vulnerable points with large fluctuations in the Caimoni index. The degree centrality of each node in the time-varying code evolution graph is calculated. Nodes with the top 20% degree centrality and whose edge connection number changes in adjacent time slices exceed a preset connection stability threshold are identified as vulnerable points with high centrality but unstable connections. Time-related dependency change patterns are analyzed to identify potential vulnerable points, resulting in a set of code vulnerability points. For the set of code vulnerabilities, a forward-looking optimization strategy is applied, which involves enhancing the abstraction layer and interface design, introducing the adapter pattern to reduce direct dependencies, applying the strategy pattern to enhance scalability, and optimizing resource management and concurrency control mechanisms to obtain forward-looking optimized code. Based on the optimization results of the forward-looking optimized code, a code adaptability analysis report is generated, which includes potential risk points and their evolution sensitivity, applied forward-looking optimization measures, long-term maintenance recommendations, and a quantitative assessment of adaptability indicators. This code adaptability analysis report is used as supplementary content to the code documentation to obtain the performance-optimized code.

15. An intelligent Scala code generation system based on artificial intelligence, characterized in that, include: The requirement parsing module is used to obtain the natural language requirement description input by the user, perform semantic understanding and information extraction on the natural language requirement description, extract functional description, input and output types and constraints, obtain a standardized set of technical elements, and map the standardized set of technical elements to the syntax structure and programming paradigm of the Scala language to obtain the requirement semantic vector. The context-aware module is used to perform static analysis on the project codebase, extract the project code structure, dependencies and coding style, construct a multi-level context knowledge graph, and use graph neural networks and attention mechanisms to process the multi-level context knowledge graph to obtain context vector representations. The code generation engine is used to fuse the requirement semantic vector and the context vector representation to obtain a joint vector representation, and to generate a code framework and core code based on the joint vector representation using a pre-trained Scala code generation model. Multiple candidate code implementation schemes are generated through a beam search algorithm, and the optimal scheme is selected to obtain the optimal code scheme. The code optimization and verification module is used to perform code formatting, type checking, performance optimization, and test verification on the optimal code solution, automatically generate documentation comments, and obtain the final and improved code.

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