Intelligent code optimization and reconstruction system based on large model

Abstract

The invention relates to the field of software engineering and computer science, in particular to an intelligent code optimization and reconstruction system based on a large model, which adopts a comprehensive code analysis module to perform static and dynamic analysis and identify potential problems and complexity indexes in codes. Through an optimization suggestion generation module, personalized optimization suggestions are generated by using a neural network algorithm, and automatic code reconstruction is realized for different programming languages. And the system ensures the functional integrity of the reconstructed code through a reconstruction verification module, and provides a user feedback mechanism to optimize a subsequent strategy. Finally, the readability, maintainability and execution efficiency of the codes are remarkably improved, and the intelligent code optimization effect is achieved.

Description

Technical Field

[0001] The present invention relates to the fields of software engineering and computer science, and in particular to an intelligent code optimization and reconstruction system based on a large model. Background Art

[0002] Code optimization and refactoring are crucial in software development. They not only improve the readability and maintainability of the code, but also significantly improve system performance and resource utilization efficiency. As the complexity of software systems continues to increase, it becomes particularly important to maintain high code quality. However, existing technologies have significant deficiencies in code optimization and refactoring. Most current tools rely on static analysis and rule-driven methods for code detection and optimization, and are unable to deeply understand the context and logic of the code. These tools typically only identify common code problems, such as syntax errors and simple performance bottlenecks, and fail to fully capture complex code smells and potential defects. In addition, traditional methods often lack intelligence and cannot dynamically adjust optimization strategies according to specific circumstances, resulting in limited optimization effects. Summary of the invention

[0003] In view of the many problems existing in the above-mentioned prior art, the present invention provides an intelligent code optimization and reconstruction system based on a large model. The present invention comprehensively uses the large model algorithm and machine learning technology to comprehensively analyze the input code, identify potential problems and generate optimization suggestions. Through automated reconstruction and intelligent feedback mechanism, the code quality and performance are significantly improved.

[0004] like Figure 1 As shown, an intelligent code optimization and reconstruction system based on a large model includes:

[0005] The code analysis module performs comprehensive static and dynamic analysis on the input code to identify potential issues, performance bottlenecks, and code smells, and generates a detailed code analysis report that includes a description of each identified issue and its possible impact;

[0006] like Figure 2 As shown in the figure, the code analysis module is the foundation of the entire solution. It conducts an in-depth review of the input code through comprehensive static and dynamic analysis. In principle, static analysis aims to find potential problems by scanning the code structure and syntax, such as fragments that do not meet programming specifications and unused variables. Dynamic analysis, on the other hand, runs the code and monitors its execution process in real time to identify performance bottlenecks and code smells (that is, non-standard practices that affect code quality). The detailed code analysis report generated by this module contains a description of each identified problem and its possible impact on program performance and maintainability. For example, if a section of code takes too long to execute at runtime, the report will point out the specific function call and give performance improvement suggestions.

[0007] The optimization suggestion generation module uses a large model algorithm to automatically generate specific optimization suggestions for identified problems based on the code analysis report. The specific optimization suggestions include: code refactoring, performance improvement, resource optimization, and security enhancement strategies;

[0008] like Figure 3 As shown in the figure, the optimization suggestion generation module is the core of the system. It uses a large model algorithm to analyze the code analysis report and automatically generates specific optimization suggestions for the identified problems. Specific optimization suggestions include code refactoring, performance improvement, resource optimization, and security enhancement strategies. For example, when it is identified that the execution efficiency of a certain loop is low, the system may recommend the use of parallel processing to improve execution efficiency, or provide more efficient algorithm alternatives for modules with large resource consumption. The effect of this module is significant and can significantly reduce the complexity of the code and improve performance.

[0009] Automatic code refactoring module, which automatically or semi-automatically refactors the code according to optimization suggestions to improve the readability, maintainability, execution efficiency and compliance with best practice standards, ensuring that the refactored code retains its original functionality;

[0010] like Figure 4 As shown in the figure, in the automatic code refactoring module, the system automatically or semi-automatically refactors the code according to the optimization suggestions to improve the readability, maintainability and execution efficiency of the code. This process ensures that the refactored code meets the best practice standards while maintaining the original functionality. For example, if the system recognizes that a section of code reuses the same logic, the automatic code refactoring module may abstract it into an independent function, thereby improving the reusability and readability of the code.

[0011] Refactoring verification module, conduct comprehensive functional testing and performance verification on the refactored code to ensure that it meets the expected standards, and generate a detailed verification report including test results and quantitative data of improvement effects;

[0012] The refactoring verification module is responsible for comprehensive functional testing and performance verification of the refactored code. It ensures that the refactored code meets the expected standards through unit testing, integration testing, and system testing, and generates a detailed verification report. The report contains quantitative data on the test results and improvement effects, such as a 30% reduction in execution time or a 20% reduction in memory usage after refactoring. This verification process not only ensures the functional integrity of the code, but also provides data support for subsequent optimization.

[0013] like Figure 5 As shown, the user feedback mechanism module provides real-time feedback to users based on the verification report, allowing users to manually adjust the reconstruction process, and records user feedback to continuously optimize subsequent reconstruction strategies.

[0014] The user feedback mechanism module provides real-time feedback to users based on the verification report, allowing users to manually adjust the reconstruction process. This mechanism is designed to record user feedback and continuously optimize subsequent reconstruction strategies to make the system more intelligent and personalized. For example, when a user is dissatisfied with an automatic reconstruction suggestion, the system will record the feedback and make adjustments in future optimization suggestions, thereby improving user experience and satisfaction.

[0015] Preferably, the code analysis module also includes a complexity assessment of the input code to generate a code complexity index, which is used to reflect the maintainability and readability of the code. Further optimization of the code analysis module includes a complexity assessment of the input code. This assessment reflects the maintainability and readability of the code by generating a code complexity index. Specifically, the complexity index can use a variety of metrics, such as cyclomatic complexity, number of lines, number of nesting levels, etc. These indicators help identify complex structures in the code, thereby providing developers with clear directions for improvement. By quantifying complexity, developers can quickly determine which code blocks need to be simplified or refactored to improve code quality.

[0016] Preferably, potential problems in the code analysis report include code snippets that do not conform to programming specifications and unused variables. Further optimization of the code analysis module includes complexity assessment of the input code. This assessment reflects the maintainability and readability of the code by generating a code complexity index. Specifically, the complexity index can use a variety of metrics, such as cyclomatic complexity, number of lines, number of nested levels, etc. These indicators help identify complex structures in the code, thereby providing developers with clear directions for improvement. By quantifying complexity, developers can quickly determine which code blocks need to be simplified or refactored to improve code quality.

[0017] Preferably, the specific optimization suggestions generated by the optimization suggestion generation module include a resource allocation optimization strategy based on performance evaluation, which is used to reduce the consumption of computing resources. In the code analysis report, the identification of potential problems includes not only code snippets that do not meet programming specifications and unused variables, but may also involve duplicate code and lengthy methods. These problems usually lead to maintenance difficulties and performance degradation during the development process. Through clear reports, the system can guide developers to prioritize the most urgent problems, thereby reducing technical debt and improving the overall health of the code.

[0018] Preferably, the algorithm used by the optimization suggestion generation module includes a neural network model to improve the accuracy of the optimization suggestion. The optimization suggestion generation module uses the neural network model to generate specific optimization suggestions, taking a resource allocation optimization strategy based on performance evaluation as an example. This strategy aims to analyze the usage of existing resources and propose a more efficient resource allocation plan on this basis, thereby reducing the consumption of computing resources. For example, when it is detected that a functional module occupies too much memory when processing a large amount of data, the system may recommend the use of data stream processing or batch processing to reduce the memory burden and improve execution efficiency.

[0019] Preferably, the automatic code refactoring module includes adapters for different programming languages ​​to support code refactoring in multiple programming languages. In the automatic code refactoring module, the system includes adapters for different programming languages ​​to ensure that the codes in multiple programming languages ​​can be effectively refactored. The implementation of this adapter relies on an abstract syntax tree (AST), which can convert the code into a language-independent representation so that the refactoring algorithm can be applied to multiple language scenarios. For example, when refactoring Java and Python code, the system can parse the corresponding AST and apply the same refactoring strategy on this basis to ensure the consistency and accuracy of the refactoring.

[0020] Preferably, the functional testing of the reconstruction verification module includes unit testing, integration testing and system testing to ensure the functional integrity of the reconstructed code. The reconstruction verification module ensures that the reconstructed code maintains functional integrity through functional testing such as unit testing, integration testing and system testing. The system automatically generates test cases and runs these tests, recording the execution results of each test. This process can effectively ensure that the reconstructed code achieves the expected performance improvement while maintaining the original function.

[0021] Preferably, the user feedback mechanism module records the user's adjustment history when the user makes manual adjustments, and generates personalized optimization suggestions based on the history. The user feedback mechanism module records the user's adjustment history when the user makes manual adjustments, so as to subsequently generate personalized optimization suggestions. For example, if the user frequently adjusts certain parameters in a specific module, the system will recognize this pattern and give priority to the adjustment of these parameters in future optimization suggestions, thereby improving the intelligence level of the system and the user experience.

[0022] Preferably, the quantitative data of the improvement effect included in the verification report includes the percentage reduction of code execution time and the percentage reduction of memory usage. The quantitative data of the improvement effect provided in the verification report, including the percentage reduction of code execution time and the percentage reduction of memory usage, provides users with a clear basis for improvement. These data can clearly demonstrate the substantial effects brought about by the refactoring, helping developers make more reasonable decisions in subsequent development and optimization.

[0023] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0024] The present invention realizes intelligent processing of code analysis by using large model algorithms and neural network technology, thereby improving the accuracy of optimization suggestions;

[0025] The present invention can not only comprehensively analyze the static and dynamic characteristics of the code, but also generate personalized optimization suggestions for specific problems;

[0026] The present invention optimizes the readability and execution efficiency of the code while maintaining complete functionality through automatic refactoring, thereby solving the defect that traditional methods cannot effectively identify complex problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a structural block diagram of the system of the present invention;

[0028] Figure 2 It is a code analysis flow chart in the present invention;

[0029] Figure 3 Generate a flow chart for the optimization suggestions in the present invention;

[0030] Figure 4 Reconstruct the verification flow chart in the present invention;

[0031] Figure 5 This is a flow chart of the user feedback mechanism in the present invention. DETAILED DESCRIPTION

[0032] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0033] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0034] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0035] The system of the present invention comprises:

[0036] Data acquisition module, preprocessing module, large model training module, including:

[0037] Data collection module: responsible for collecting front-end code from data sources such as front-end code libraries, development communities, and online code repositories. Use crawler technology to collect open source front-end project code from platforms such as GitHub and GitLab. Collect official documentation and best practices for front-end frameworks (such as React, Angular, and Vue.js). Collect discussions and code examples from front-end development communities (such as CSDN, StackOverflow, and Reddit).

[0038] Preprocessing module: Clean, standardize and extract features from the collected code. Code cleaning: Remove useless comments, spaces, logs and other non-critical codes. Code standardization: Unify the code style, such as using tools such as Prettier and ESLint. Feature extraction: Extract the structured features of the code (such as cyclomatic complexity calculation, code duplication calculation, etc.) and unstructured features (such as code snippets).

[0039] Large model training module: Use preprocessed data to train deep learning models. Select pretrained models suitable for processing natural language and programming languages, such as BERT, GPT-3, CodeBERT, etc. Fine-tune the pretrained model and use the front-end code dataset for training to learn the syntax, semantics, and best practices of the front-end code. Training goals include code defect detection, code optimization suggestions, and code style unification.

[0040] The code analysis module performs comprehensive static and dynamic analysis on the input code to identify potential issues, performance bottlenecks, and code smells, and generates a detailed code analysis report that includes a description of each identified issue and its possible impact;

[0041] The optimization suggestion generation module uses a large model algorithm to automatically generate specific optimization suggestions for identified problems based on the code analysis report. The specific optimization suggestions include: code refactoring, performance improvement, resource optimization, and security enhancement strategies;

[0042] Automatic code refactoring module, which automatically or semi-automatically refactors the code according to optimization suggestions to improve the readability, maintainability, execution efficiency and compliance with best practice standards, ensuring that the refactored code retains its original functionality;

[0043] Refactoring verification module, conduct comprehensive functional testing and performance verification on the refactored code to ensure that it meets the expected standards, and generate a detailed verification report including test results and quantitative data of improvement effects;

[0044] The user feedback mechanism module provides real-time feedback to users based on the verification report, allowing users to manually adjust the reconstruction process, while recording user feedback to continuously optimize subsequent reconstruction strategies.

[0045] The system of the present invention performs the following steps:

[0046] Step 1: System Initialization

[0047] Environment construction: First, install and configure the necessary software environment on the server, including deep learning frameworks (such as TensorFlow or PyTorch), code parsing tools (such as Babel or ESLint), database systems, etc. Select appropriate large model architectures (such as Transformer-based models), development frameworks (such as React, Vue.js) and tools (such as ESLint, Prettier).

[0048] Model loading: Load a pre-trained large-scale code analysis model from the storage medium. This model has been trained on a large number of open source code projects and has the ability to identify features such as code style, performance bottlenecks, and design patterns.

[0049] Interface configuration: Configure the user interface, including the code upload interface, optimization suggestion display area, refactoring execution button, etc., so that users can interact with the system.

[0050] Step 2: Code parsing and feature extraction

[0051] Code upload: Users upload the front-end project source code to be optimized through the user interface.

[0052] Code parsing: The system uses the code parsing module to parse the uploaded code. The key steps are as follows:

[0053] 1) Code preprocessing

[0054] Clean the data, remove useless comments, blank lines, etc. Standardize the code style to ensure data consistency.

[0055] 2) Construct a prediction analysis table:

[0056] First, we need to calculate the First set of each non-terminal symbol, that is, the set of the first symbol of all derived symbol strings of the non-terminal symbol.

[0057] Then, we need to calculate the Follow set, especially for the non-terminals that derive the empty string ε. The Follow set is used to determine the terminal symbols that follow these non-terminals.

[0058] Use the First and Follow sets (if necessary) to populate the predictive analysis table.

[0059] 3) Execute the parsing process:

[0060] A stack is used to simulate the derivation process, and the initial content of the stack is the start symbol.

[0061] Scan the input string of symbols from left to right.

[0062] At each step, according to the non-terminal symbol at the top of the stack and the next input symbol, the corresponding action is looked up in the prediction analysis table (pushing the non-terminal or terminal symbol on the right side of the production rule, or reporting an error).

[0063] The above steps are repeated until the stack is empty and all input symbols are successfully processed to generate the intermediate representation abstract syntax tree AST of the code.

[0064] Feature extraction: The feature extraction module extracts key features from the parsed code. These features include function call relationships, variable usage frequency, cyclomatic complexity, resource loading methods, etc. The results of feature extraction will be encoded into vector or matrix form for subsequent model processing. Various indicators of the code are calculated, such as cyclomatic complexity, number of lines of code, function length, etc.

[0065] 1. Cyclomatic complexity calculation algorithm

[0066] -Cyclomatic complexity is a measure of code complexity that represents the number of independent paths through a program.

[0067] -The calculation formula is: V(G)=E-N+2P, where V(G) represents the cyclomatic complexity, E represents the number of edges in the program control flow graph, N represents the number of nodes, and P represents the number of connected components.

[0068] -The deduction steps are as follows:

[0069] -First, convert the front-end code into a program control flow graph.

[0070] -Count the number of edges E and nodes N in the control flow graph.

[0071] -Determine the number P of connected components.

[0072] -Substitute the values ​​of E, N, and P into the formula to calculate the cyclomatic complexity.

[0073] 2. Code duplication calculation algorithm

[0074] -Code duplication is a measure of the proportion of repeated code snippets in the code.

[0075] -The calculation formula is: repetition degree = length of repeated code segment / total code length.

[0076] -The deduction steps are as follows:

[0077] -Perform word segmentation on the front-end code and divide the code into individual code snippets.

[0078] -Use the hash algorithm to calculate each code snippet and get a hash value.

[0079] -Count the number of code snippets with the same hash value, which is the length of the repeated code snippet.

[0080] - Calculate the total code length.

[0081] - Divide the length of the repeated code fragment by the total code length to get the code duplication.

[0082] Step 3: Large model analysis and optimization suggestion generation

[0083] Large model input: The extracted code features are input into a pre-trained large-scale code analysis model.

[0084] Large model training and inference:

[0085] During the training phase of the large model, a large-scale code corpus was used for pre-training. The model generated code based on an autoregressive mechanism and learned different code patterns from it. In order to improve the model's understanding of the optimization strategy, a supervised learning module was further added to guide the model's reasoning process.

[0086] By minimizing this loss function, the model can gradually optimize its code reasoning ability.

[0087] Loss function formula:

[0088] For the binary classification problem, the expression of the cross entropy loss function is:

[0089]

[0090] For multi-classification problems, the expression of the cross entropy loss function is:

[0091]

[0092] in:

[0093] L(θ) is the loss function, which is a function of the model parameters θ.

[0094] N is the total number of samples.

[0095] C is the total number of categories.

[0096] y i is the true label of the i-th sample. For binary classification problems, it takes a value of 0 or 1. For multi-classification problems, it is a one-hot encoded vector.

[0097] It is the predicted probability of the model for the i-th sample. For binary classification problems, the value is between 0 and 1; for multi-classification problems, it is a probability distribution vector.

[0098] log stands for natural logarithm.

[0099] These two expressions are applicable to binary classification and multi-classification problems respectively. By minimizing these loss functions, the model can gradually optimize its code reasoning ability.

[0100] Code optimization derivation:

[0101] The optimization goal is to maximize the execution efficiency of the code, and the optimization process is based on a series of heuristic rules for reasoning. Through the dynamic programming algorithm, the global optimal optimization path can be calculated. A typical example of a dynamic programming algorithm is solving the shortest path problem, such as using the Floyd-Warshall algorithm to calculate the shortest path between all pairs of points. The basic formula of the algorithm is as follows:

[0102] dist[i][j]=min(dist[i][j], dist[i][k]+dist[k][j])

[0103] Where: dist[i][j] represents the shortest path length from point i to point j.

[0104] k is an intermediate point, and the algorithm will consider updating the shortest path from i to j through all possible intermediate points k.

[0105] This formula is an example of a state transition equation in a dynamic programming algorithm, which describes how to build solutions to a larger problem from known solutions to subproblems. By iteratively applying this formula, the algorithm is eventually able to calculate the global optimal optimization path.

[0106] Optimization suggestion generation: Based on the analysis results of the model, the optimization suggestion generation module generates specific optimization suggestions. These suggestions include code refactoring solutions, performance optimization measures, code style adjustments, etc. At the same time, the module will also sort and prioritize the suggestions so that users can choose as needed.

[0107] Step 4: Optimization suggestion display and user confirmation

[0108] Suggestion display: The system displays the generated optimization suggestions to the user through the user interface. The displayed content includes a detailed description of the suggestion, a comparative display of code snippets, and an evaluation of the expected effect.

[0109] User confirmation: Users evaluate the optimization suggestions displayed and decide whether to adopt them. For the adopted suggestions, users can confirm them by clicking the operation button on the interface.

[0110] Step 5: Refactoring execution and effect evaluation

[0111] Refactoring execution: After the user confirms the optimization suggestion, the system applies the suggestion to the code through the refactoring execution module. The refactoring process includes adjusting the code structure, renaming variables, splitting and merging functions, etc. The system can also provide automated or semi-automated tools for code refactoring to reduce the user's workload.

[0112] Effect evaluation: After the refactoring is completed, the system will compare and analyze the code before and after the refactoring to evaluate the refactoring effect. The evaluation indicators include code readability, maintainability, execution efficiency, etc. The evaluation results will be fed back to the user so that the user can understand the actual effect of the refactoring.

[0113] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0114] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. An intelligent code optimization and reconstruction system based on a large model, characterized in that: include: The code analysis module performs comprehensive static and dynamic analysis on the input code to identify potential issues, performance bottlenecks, and code smells, and generates a detailed code analysis report that includes a description of each identified issue and its possible impact; The optimization suggestion generation module uses a large model algorithm to automatically generate specific optimization suggestions for identified problems based on the code analysis report. The specific optimization suggestions include: code refactoring, performance improvement, resource optimization, and security enhancement strategies; Automatic code refactoring module, which automatically or semi-automatically refactors the code according to optimization suggestions to improve the readability, maintainability, execution efficiency and compliance with best practice standards, ensuring that the refactored code retains its original functionality; Refactoring verification module, conduct comprehensive functional testing and performance verification on the refactored code to ensure that it meets the expected standards, and generate a detailed verification report including test results and quantitative data of improvement effects; The user feedback mechanism module provides real-time feedback to users based on the verification report, allowing users to manually adjust the reconstruction process, while recording user feedback to continuously optimize subsequent reconstruction strategies.

2. The intelligent code optimization and reconstruction system based on a large model according to claim 1 is characterized in that: The code analysis module also includes a complexity assessment of the input code to generate a code complexity index, which is used to reflect the maintainability and readability of the code.

3. The intelligent code optimization and reconstruction system based on a large model according to claim 1 is characterized in that: Potential issues in the code analysis report include code snippets that do not comply with programming standards and unused variables.

4. The large model-based intelligent code optimization and reconstruction system according to claim 1 is characterized in that: The specific optimization suggestions generated by the optimization suggestion generation module include resource allocation optimization strategies based on performance evaluation, which are used to reduce the consumption of computing resources.

5. The large model-based intelligent code optimization and reconstruction system according to claim 1 is characterized in that: The algorithm used by the optimization suggestion generation module includes a neural network model to improve the accuracy of the optimization suggestions.

6. The intelligent code optimization and reconstruction system based on a large model according to claim 1 is characterized in that: The automatic code refactoring module includes adapters for different programming languages ​​to support code refactoring in multiple programming languages.

7. The large model-based intelligent code optimization and reconstruction system according to claim 1 is characterized in that: The functional testing of the reconstruction verification module includes unit testing, integration testing and system testing to ensure the functional integrity of the reconstructed code.

8. The large model-based intelligent code optimization and reconstruction system according to claim 1 is characterized in that: When the user makes manual adjustments, the user feedback mechanism module records the user's adjustment history and generates personalized optimization suggestions based on the history.

9. The intelligent code optimization and reconstruction system based on a large model according to claim 1 is characterized in that: The validation report contains quantitative data on the improvement effect, including the percentage reduction in code execution time and the percentage reduction in memory usage.

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