2562 results about "Code generation" patented technology

The invention discloses an intelligent programming auxiliary method and system based on a multi-modal AI language model, and belongs to the technical field of programming auxiliary tools. The method comprises the following steps: a multi-modal input processing stage; a dynamic context modeling stage; a hierarchical semantic analysis stage; in the code generation stage, codes are generated in two stages by adopting a Codex-Plus large model; the reinforcement learning driven code optimization stage is used for carrying out multi-objective optimization and reward function design on the codes generated in the code generation stage; a multi-dimensional feedback stage; an interaction and visualization stage; code semantic deep analysis, dynamic context sensing, multi-target optimization generation and real-time interactive feedback are realized by fusing code texts, natural language description, developer behavior data and a domain knowledge graph, and programming efficiency and code quality can be remarkably improved.

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

A method, apparatus, and computer-readable medium of a migration platform for analyzing a legacy database based on applying a large language model to the legacy database and legacy application code to extract legacy database values and legacy application values, determining destination database values corresponding to a destination database based on the legacy database values and the legacy application values, generating migration code for migrating the legacy database to the destination database based on code generation templates, user preferences, and code generation procedures configured to create one or more files and one or more directories based on the code generation templates, validating the migration code based on generating mock data, storing the mock data in the legacy database, and applying the migration code to the mock data to migrate the mock data to the destination database, and executing the validated migration code to perform a migration of the legacy database.

A system, method, and computer-program product includes identifying a plurality of code synthesis items for a target programming language, generating a code synthesis prompt based on a first sampling of the plurality of code synthesis items, synthesizing, via a large language model, a plurality of raw code segments using the code synthesis prompt, executing the plurality of raw code segments with a code interpreter associated with the target programming language, determining one or more valid code segments of the plurality of raw code segments that the code interpreter successfully executed, aggregating, via a second sampling, the one or more valid code segments into one or more validated code synthesis training samples, and training a code generation model using the one or more validated code synthesis training samples. User interfaces may be provided to allow target coding tasks to be specified via text or speech.

The invention discloses an unmanned aerial vehicle flight control system vulnerability detection method based on data flow analysis and LLM, and belongs to the technical field of intelligent software testing. Comprising the following steps: extracting a code function module associated with user operation in an unmanned aerial vehicle flight control system through a data flow analysis method, and establishing an operation-code mapping relation library; generating a structured natural language semantic description for each function module code by adopting a large language model LLM, and forming a multi-dimensional semantic feature vector; based on correlation analysis of multi-module semantic features, a combined test scene is constructed, and a natural language test case is generated; the natural language test case is converted into an executable test code through reverse semantic mapping, and coding reconstruction of test logic is completed; and executing a test code and capturing a runtime log in an unmanned aerial vehicle simulation environment, and performing vulnerability feature extraction and root cause positioning by using a large language model. According to the method, the efficiency is improved, and meanwhile, the deep coverage test of a complex interaction scene is supported.

Computer-implemented systems and methods use a Large Language Model (LLM) for converting a legacy computer program in a first language to a human-language description of the legacy computer program, which description can be validated as being an accurate description of the legacy computer program. Once validated, the human-language description can be converted, again using an LLM, to a computer program in a target programming language. An LLM can also be used to generate test scripts for the new target-language program to test the performance of the target-language program in a production environment. An LLM can also be used to reconcile outputs from the legacy program to the new target program, such as on a function-by-function basis. If the differences between the outputs (if any) are sufficiently negligible, the legacy computer program can be decommissioned, and the new, target language program can be used in production.

The invention relates to a code generation method and system based on heterogeneous multi-agent collaboration and electronic equipment. According to the method, a special commentator agent instantiated by a heterogeneous large language model is added into a multi-agent collaboration process; according to the method, consistency and feasibility verification is carried out on a demand document given by an analyst agent by adopting a commentator agent, a modification suggestion is proposed in an early stage, deviation and problems are found in time in the early stage of a task, the cost and complexity of later correction are reduced, the robustness and reliability of codes are improved, and the method is suitable for large-scale popularization and application. And the generated result can still maintain high-level correctness under extreme conditions or specific boundary conditions. And the method has practical significance in improving software development efficiency, reducing project risks and improving the quality of an intelligent automatic solution.

The invention relates to the technical field of code security analysis, and discloses a control flow analysis and retrieval enhancement-based large model code security review method, which comprises the following steps of: analyzing an incremental code to generate an abstract syntax tree and data flow analysis information; retrieving local knowledge base association business rules and historical vulnerabilities based on grammatical features, and generating a context enhancement prompt; calling a large language model to jointly analyze codes and contexts, identifying vulnerabilities and outputting a structured report; and combining with a historical false alarm data optimization result and then integrating to a development assembly line. Multi-source information is fused through an RAG technology to enhance semantic understanding, and control flow node tracking and cross-version semantic association are combined, so that the problems of a traditional tool business logic vulnerability detection blind area, incremental code analysis failure and high false alarm rate are solved, synchronous improvement of security examination accuracy and efficiency is realized, and the method is adaptive to an agile development scene.

The invention discloses an ST code generation method based on graph alignment coding large model and multi-agent collaboration, and the method comprises the steps: receiving an ST code programming demand inputted by a user through a demand analysis module, refining and analyzing the demand based on multiple rounds of interactive conversations of the user and insight agents, and generating a standardized ST code programming demand; the retrieval module receives a standardized ST code programming requirement, and retrieves and obtains related knowledge through a retrieval agent in combination with an ST code knowledge base; and the double-agent collaborative self-correction code generation module receives standardized ST code programming requirements and retrieved related knowledge, a graph alignment coding large model constructed based on a graph neural network and a cross-modal alignment technology serves as a coding agent to cooperatively work with a review agent, ST code structure information is injected into the large model, and a final ST code is generated. According to the method, high-accuracy and high-reliability ST code automatic generation can be realized, and the development efficiency of a PLC program in the industrial control field is improved.

The invention discloses a code generation method and device based on LLM multi-agent cooperation and computer equipment. The method comprises the following steps: acquiring a user programming demand; analyzing the user programming demand based on LLM and decomposing a programming task to generate a task tree; selecting a to-be-allocated sub-task from the task tree, and evaluating the matching degree of an intelligent agent in an intelligent agent pool and the to-be-allocated sub-task based on LLM to generate a task allocation instruction; sending the task allocation instruction to a corresponding target agent, so that the target agent executes the to-be-allocated sub-task, and feeding back an execution result; and outputting the execution result. By implementing the method provided by the invention, the programming efficiency and quality can be improved, the resource allocation can be optimized, and the efficient development of complex requirements and large projects can be supported.

The invention discloses a CAPEC vulnerability management system based on a large language model, and belongs to the technical field of network security. The system comprises three modules: a vulnerability-CAPEC dynamic mapping module jointly encodes vulnerability description and code context through a bimodal large language model, accurately associates vulnerability logic with a CAPEC attack mode in combination with comparative learning and knowledge graph construction, and breaks through semantic limitation of traditional rule matching; the adversarial repair code generation module is used for generating high-robustness repair codes through adversarial training and syntax tree verification by fusing CAPEC relieving suggestions and code features on the basis of the association result, so that the secondary vulnerability risk is remarkably reduced; and the full-process automatic verification and DevOps integration module performs multi-dimensional security verification such as symbolic execution, fuzzy testing and the like on the generated repair code, and deeply integrates a development tool chain to realize real-time pushing and closed-loop management of a repair scheme.

The invention relates to the technical field of semantic recognition, can be applied to business scenes such as financial science and technology and medical health, and discloses an interactive code generation method, device and equipment and a medium. And a visual preview interface is generated in combination with the context, multi-round modification through interactive adjustment or text editing is supported, and finally user interface element codes are generated. According to the method, natural language processing, context sensing and visual interactive correction are combined, so that the closed-loop process of interface codes from generation to dynamic adjustment is realized, the customization degree and the service adaptability of a generated result are improved, and the problems that an existing tool template is rigid, semantic understanding is deviated, and a multi-round correction mechanism is lacked are solved.

Provided herein is a method for reviewing code. The method can comprise parsing the code to generate a structural representation of the code, wherein the structural representation comprises a graph representative of the code. The method can comprise processing the code and the structural representation to generate a context for the code based at least in part on the graph. The method can comprise processing the context and a set of prompts to generate a set of contextualized prompts, wherein at least two prompts in the set of prompts are specific for different categories of issues. The method can comprise prompting a first set of language models with the set of contextualized prompts to generate a set of issue reports. The method can comprise prompting a second set of language models to generate a set of validated issue reports comprising a set of non-hallucinated issue report.

The invention discloses an automatic control code generation and verification method and device, equipment and a storage medium, and relates to the technical field of automatic control, the method is applied to a large language model, and a natural language command is received; performing matching retrieval on the vector database according to the natural language command to obtain an example code snippet; obtaining API structured information corresponding to the example code snippets from a knowledge graph database; generating an initial control code based on the example code snippet and the API structured information; and performing multi-stage virtual operation verification on the initial control code in the software motion control system, and generating a target control code according to a multi-stage verification result confirmed by a user for multiple times and the initial control code. According to the method, the initial control code is automatically generated through double-database retrieval based on the large language model, then multi-stage code verification is performed through the software motion control system, the target control code is generated in combination with verification results confirmed by a user for multiple times, and the code generation speed and reliability are improved.

The invention provides a business module source code generation method and system based on large model business reasoning, and belongs to the technical field of code generation. The business module source code generation method comprises the steps that semantic analysis is conducted on business requirements, and a business association graph with weights is constructed; performing module division, and decomposing a complete task into a plurality of sub-tasks; calling a coordination agent, distributing a corresponding module code generation agent based on a large model for each decomposed subtask, and injecting context information to generate a corresponding module code; wherein the module code generation agent learns a corresponding relationship between tasks and injected context information and codes in advance based on field self-adaptive staged training; and calling the verification agent to perform code quality verification. The method has the beneficial effects that the module division is performed based on the business association graph with the weight, the multi-agent collaborative code generation is constructed by adopting the field-adaptive staged training, and the quality of the generated code is verified, so that the code generation efficiency and quality are improved.

The invention relates to the technical field of data processing, in particular to a data processing method and equipment. In the embodiment of the invention, a target problem input by a user is solved through a multi-agent mode, a task planning agent can independently think and understand the requirements of the user and split service analysis logic according to target information and the target problem, and the target problem of a natural language is converted into each to-be-executed sub-task; and then the code generation agent is called to generate a code corresponding to each subtask, and each code is executed to obtain a target processing result for the target problem, so that the efficiency and accuracy of data processing are improved.

The invention discloses a method and a system for realizing automatic code generation by using multiple AI Agents, and relates to the field of electric digital data processing, a test driving agent receives a task transmitted by a previous node, and generates a task number, a task description, test data, a unit test script and a code statement; the code generation agent generates or updates codes according to the received task description and code declaration; the test driving agent returns an integrated test script and test data according to the task number; the continuous integration agent performs integration verification and regression test and feeds back information; the test driving agent returns a function test script, a performance test script and test data according to the task number; and the continuous deployment agent carries out continuous delivery and deployment and feeds back information. By means of mutual conversation and cooperative work of multiple AI Agents and in combination with a feedback mechanism, the problem that a single large model cannot complete complex software development tasks is solved, full-process operation automation can be achieved, and the code generation quality and efficiency can be improved.

The invention discloses a parameter feature-based test case generation method and a related product. In the scheme, a parameter document of target simulation software is obtained; and extracting parameter feature information of the target simulation software based on the parameter document. And on the basis of the parameter feature information, matching a test case template and a code generation rule in a pre-constructed code knowledge base, and generating an initial test case code. And dynamically optimizing the initial test case code according to feedback data generated in a simulation test process to generate a target test case code. According to the technical scheme, the parameter feature information is automatically extracted, the complex dependency relationship between the parameters is accurately analyzed, the test case is automatically generated based on the knowledge base matching template and rule, software changes are adapted in real time in combination with a dynamic optimization mechanism, dynamic and efficient generation of test case codes is achieved, and the test efficiency is improved. The coverage degree and the adaptability of the test case codes are improved, and then the accuracy, the comprehensiveness and the efficiency of the test are improved.

The invention relates to the technical field of large model application, and discloses a code generation task adaptive reasoning method, device and equipment. The method comprises the following steps: obtaining a problem description of a code generation task, and converting the problem description into a pseudo code based on a large language model; according to the problem description and the corresponding pseudo code, based on a pre-trained complexity evaluation model, performing complexity evaluation on the code generation task; the complexity evaluation model is obtained through training by taking problem description and pseudo codes as input and task complexity as output; a reasoning mode is matched according to the complexity evaluation result, a set reasoning model is adopted, and a code generation task is executed based on the matched reasoning mode; wherein different complexities correspond to reasoning modes of different reasoning depths. According to the method, the dynamic perception of the large language model on the task complexity is realized, so that the reasoning depth and the resource allocation can be adaptively adjusted according to the actual demand of the task.

The invention relates to a method and a device for generating software based on a large model technology, a medium and electronic equipment, and relates to the technical field of software generation, and the method comprises the following steps: acquiring demand information of a user for software generation; generating a configuration definition file corresponding to the large model generation demand information through a preset code, wherein the configuration definition file is a configuration file of a domain-specific language definition device; performing regular format reformatting processing on the configuration definition file to obtain a processed file; based on the processed file, determining a service flow logic corresponding to a service function in the to-be-generated software, and based on the service flow logic, generating a target software code corresponding to the to-be-generated software through a code generation large model, the format of the target software code being a configuration file format of the domain-specific language definition device; and based on the target software code, through a preset rendering engine, generating a front-end page and a rear-end page of the software needing to be generated. The method has the effect of improving the software development efficiency.

The invention discloses a code generation system based on a natural language and meta-model drive, and the system comprises an AI layer which comprises a plurality of agents which work cooperatively, and is configured to receive a natural language instruction, and carry out the intention recognition, entity extraction, context analysis and constraint verification through the agents, so as to generate structured metadata; the meta-model engine is configured to be used for constructing and instantiating one or more meta-model objects according to the structured metadata generated by the AI layer; and the code template engine is configured to be used for matching one or more corresponding code templates from the code template library according to the content and the type of the meta-model object, and generating executable codes or configuration files of one or more target languages through rendering. By means of the scheme, the natural language is converted into the meta-model decoupled from the technology through the AI, and then the high-quality standardized code is automatically generated through the template engine, so that the development efficiency is improved, and developers can focus on core business innovation.

The invention discloses a structured text code generation and review method, which comprises the following steps of: receiving a control task demand input by a user, supporting natural language, structured form or graphical process input, performing deep semantic analysis by utilizing a demand analysis module, identifying control conditions, control actions and constraint factors, and generating and reviewing a structured text code. The task is converted into a plurality of control units through the SCoT chain; the SCoT chain reader generates a semantic vector according to a task demand, the AST reader generates AST representation through a grammar rule structure and a hierarchical relationship, and the decoder predicts a next grammar rule and generates an ST code in combination with the SCoT chain and AST features; and verifying grammar and logic of the ST code through an interactive automatic review and repair mechanism, if compiling errors occur, analyzing errors by the teacher model and providing repair suggestions, and performing code repair by the student model according to guidance until the code conforms to an expected function and passes compiling.

The invention discloses an intelligent data acquisition and conversion method based on a visual rule model, which comprises the following steps: S1, converting a configured rule chain into a data body in a JSON format by a visual rule modeling layer, analyzing the data body into an AST abstract syntax tree, and performing field mapping on the AST abstract syntax tree, the mapping fields are converted into executable codes through an intelligent conversion engine, and an execution plan is generated; s2, the intelligent acquisition layer pulls data from the heterogeneous data source, cleans the data and then allocates the data to an intelligent conversion engine for parallel processing; and S3, the intelligent conversion engine monitors the data consanguinity map and the field-level conversion path in real time, and sends a signal to the visual rule modeling layer or the intelligent acquisition layer according to the abnormal condition type so as to realize rule chain reconfiguration or data reacquisition. The method has the advantages that non-coding docking and real-time supervision of heterogeneous system data are realized, and the problems of dynamic adaptation, semantic understanding and data blood relationship interpretability in financial fund management are solved.

The invention provides a multi-round automatic machine learning agent system based on reinforcement learning optimization. Comprising a task analysis module used for generating an initial prompt for an MLE agent to call; the MLE agent module is used for generating an executable code; the code executor is used for generating an execution result; the evaluator is used for outputting a normalized value of each index and a code correctness identifier; the reward construction module is used for generating a reward value; the reinforcement learning optimizer is used for calculating group average return and candidate advantages and updating strategy parameters of the MLE intelligent agent module based on the candidate advantages; and the multi-round interaction control module is used for feeding back the execution result of the previous round and the reward value to the MLE agent module in the multi-round interaction process, and controlling code generation of the next round until a preset termination condition is met. According to the invention, strategy adaptive evolution, reinforcement learning optimization of fine-grained credit distribution and multi-round closed-loop automatic process improvement can be realized.

The invention discloses an automatic code security optimization method based on a large language model, and belongs to the technical field of code security optimization. Acquiring large-scale programming data to establish a programming data set, establishing a primary training model, generating an initial code and establishing an initial code set; the predefined vulnerability mode set scans the initial code set, vulnerabilities in the initial code set are identified and defined as initial vulnerabilities, and the initial vulnerabilities are positioned and classified; static analysis is matched with a semantic discriminator to evaluate the code snippets to obtain a new generation strategy, and the primary generation strategy is adjusted to obtain an optimized training model; capturing potential vulnerabilities as training signals, and optimizing the optimization training model to obtain a continuous optimization training model; and establishing a vulnerability library and a repair strategy library, and optimizing vulnerability time and a repair algorithm by analyzing data of the vulnerability library and the repair strategy library. Potential vulnerabilities in generated codes can be automatically recognized and repaired, a large amount of manual intervention is not needed, and code generation safety is improved.

Provided in the present application is a code generation method, comprising: a code development platform receives input information by a user in a first code file, extracts context information of the input information according to a data warehouse to which the first code file belongs, and retrieves a business knowledge base corresponding to the data warehouse according to the input information so as to obtain target business knowledge; then the code development platform combines the input information, the context information and the target business knowledge according to a prompt template, so as to obtain prompt information; and then the code development platform inputs the prompt information into a large language model (LLM) for reasoning, and presents to the user a code segment reasoned by the LLM. Thus, using the prompt information from the same data source in different processes for prompting the LLM can achieve complete sharing and complete alignment of the prompt information, thereby improving the accuracy of generating code in single time. Furthermore, using a unified prompt template to combine the context information and the business knowledge can make a code segment generated by the LLM more accurate, thereby improving the code acceptance rate in professional domains.

The invention provides a robot control method and system based on a big language model, a double big language model evaluation mechanism is adopted, a big language model of a code generation and modification module uses Claide 3.5 sonnet, control codes are generated by the Claide 3.5 sonnet, evaluation is provided by adopting a gpt-4o evaluation model of OpenAI to optimize suggestions, and the big language model of the code generation and modification module is used for evaluating the control codes. The risk that problems are difficult to find due to homogenization of the same module is reduced, and the generation quality, safety and supportability are improved; in addition to a double large language model evaluation mechanism, due to the fact that safety prompt words are added into prompt words of a generation model and a generation template example is provided, operation of the generation model in a specific framework can be limited, the possibility of error occurrence is reduced, and specification and safety of a generation effect are further provided; in addition, the control class verification module constructs a simple simulation environment after the control class codes are generated, so that grammar errors and logic vulnerabilities of the generated codes can be screened in advance, and potential safety hazards are further reduced.

An integrated development environment (IDE) leverages a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text. The industrial IDE can also analyze written code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.

The invention provides a security code automatic generation method and system based on a full information theory and mechanism ambiguity, and the method comprises the steps: S1, carrying out the feature extraction of a grammar verification layer of an input demand, extracting the grammar features of a code through the topological features of an abstract syntax tree, and carrying out the formalized rule verification of the extracted grammar features; s2, constructing a semantic association model of a cross-code snippet through a semantic analysis layer, and forming a propagation path for tracking a data flow and a control flow; s3, in the pragmatic optimization layer, combining pragmatic constraints of the business scene, and generating a security policy through a dynamic utility function; and step S4, optimizing parameters and security policies of the dynamic utility function through knowledge federal evolution and a closed-loop feedback mechanism. According to the method, a deep collaboration mechanism of grammar, semantic and pragmatic three-dimensional information and a dynamic strategy generation model are reconstructed, security logic is converted from external attributes to endogenous cognitive competence, and the security, interpretability and adaptability of automatic code generation are improved.

The invention relates to the technical field of code generation, and provides a front-end code generation method and system based on a large language model and electronic equipment. According to the application, the business demand instruction is structured through the semantic analysis result corresponding to the business demand instruction to obtain the structured cue word, and the component library document carrying the component specification is set in the component-level cue word generator, so that the vectorized component library document is retrieved according to the semantic analysis result to generate the specification cue word, and the user experience is improved. Therefore, the structured cue word and the standard cue word are fused to obtain a fused cue word for code generation, and a front-end code corresponding to the business demand instruction is obtained, so that the large language model can be combined with the standard cue word to automatically adapt to fine-grained code specifications such as a file structure, a coding specification and a component dependence management rule; therefore, the problem that code specifications cannot be introduced into a large language model is solved, the number of times of secondary development and debugging is reduced, and the generation efficiency of front-end codes is improved.