207 results about "Code review" patented technology

The invention provides a large language model-driven code review and optimization method, which belongs to the field of computer science, and comprises the following steps of: firstly, performing intensive training on a model through rich code samples and accurate manual annotation to ensure that the model has accurate code understanding and analysis capability; and then, performing deep analysis on an input source code by utilizing the trained large language model, and constructing an abstract syntax tree (AST) to capture deep semantic information of the code. And finally, the model automatically analyzes the codes, identifies potential defects, non-standard coding practices and optimization possibilities, and generates specific optimization suggestions, including code repair schemes and guidance following optimal practices, according to identification results, so that efficient support is provided for developers, and the quality of software products is ensured.

The invention discloses an auxiliary code review method and system based on a large language model, and the method comprises the steps: analyzing a submitted code file based on a static analysis technology and an abstract syntax tree AST extraction tool, and extracting key elements; reviewing the code through a pre-constructed company internal and external knowledge base by relying on a knowledge graph technology to obtain a reviewing result; performing context analysis on the code by adopting a large language model to obtain an analysis result; integrating the review result and the analysis result, and generating a review report through NLP; a containerization technology is adopted to be integrated with plug-ins of mainstream development tools, and private deployment is supported. According to the method, the efficiency, accuracy and safety of code review can be greatly improved, the workload of manual review is reduced, and automation and intelligentization of the code review process are realized.

The embodiment of the invention provides a code exception analysis method and device, a storage medium and a program product. In the embodiment of the invention, in the preset plug-in, the global knowledge graph is constructed through the logic syntax tree of the plurality of original business codes, so that intelligent anomaly analysis is carried out on the modified to-be-detected codes, multi-language and heterogeneous programming styles and complex dependency limitation are broken through, and cross-language and cross-file analysis is supported; change code snippets and associated influence code snippets thereof are accurately identified, and a data basis is tamped and analyzed; the analysis depth is deepened and the accuracy is improved through a multi-dimensional anomaly analysis large model and four-dimensional collaborative analysis in combination with an adaptive review rule and a knowledge graph; and finally, performing correction based on the generated repair scheme. Through the technical fusion of an MCP protocol, a multi-AI model, a syntax tree and a knowledge graph, the high efficiency, accuracy and real-time performance of code examination and repair are realized, and the service development quality and the system operation stability are remarkably improved.

The invention relates to the technical field of data processing, and discloses an intelligent code review method and system based on a large language model, and the method comprises the steps: obtaining a to-be-reviewed code, carrying out the lexical analysis, grammatical analysis and semantic analysis of the to-be-reviewed code, extracting the key information of the code, and obtaining a preprocessed code; multiple features of the code are extracted from the preprocessed code, and target code features are obtained; inputting the target code features into a large language model, and generating a code review result through the large language model; according to the method, complex code semantics and logic problems can be processed, the code review accuracy is improved, and omission and errors of manual review are reduced.

The invention provides a DDD architecture service code review and evaluation method based on a multi-expert LLM, and belongs to the technical field of industry intelligence and the field of software process quality assurance. Multi-expert large model cooperation, token polling transmission and specific scene domain drive design (DDD) architecture analysis are adopted; in the same submitted code group, different dominant large models can be tried to serve as dominant large models, and final code review is performed to optimize an access condition of a suggested number. And through a multi-expert large model cooperation mechanism, the environment problem easily generated by a single large model is avoided, and the review stability is improved. The cooperation efficiency between large models is improved through a token polling mechanism of a large model expert group, fair speaking of each large model is ensured, an online state detection mechanism of a subordinate large model can ensure that tokens are transmitted to currently available large models, the situation that the overall review process is affected by individual large models is avoided, and the review efficiency is improved. And the business code review efficiency of the system is effectively improved.

The invention discloses an intelligent code change recognition system, method and device based on a large language model and a medium, and relates to the field of software code review and large language models. The system comprises a code monitoring module, a prompt word module, a large language model module and a user-defined rule module. The code monitoring module monitors a code library event and transmits information; the cue word module generates natural language cue words; the large language model module gives a change recognition result based on the cue words and the code review knowledge base; and the custom rule module manages personalized review rules. A code pushing event is monitored through the code monitoring module, the code pushing event is analyzed by the large language model module after being processed by the prompt word module, whether a change request is created or not is intelligently judged, and a knowledge base is updated by utilizing a historical review result. According to the method, unreasonable change requests are reduced, the review workload is reduced, the accuracy and adaptability are improved, and personalized requirements are met.

The invention discloses a full-process automatic unit testing method, device and equipment for an AI chip operator, a medium and a product. The method comprises the following steps: generating an engineering directory and a test framework design document through an architect agent according to operator interface information and a DSL operator design document knowledge base; the method comprises the following steps of: generating a test code for unit test by a software engineer agent according to a test framework design document and a domain knowledge base, and storing the test code in an engineering directory; multiple rounds of interaction are carried out through a software engineer agent and a test engineer agent, a test code is gradually corrected into a target test code which can be successfully compiled, a generated test case can be successfully executed and the generated test case meets the code coverage rate requirement, and a target operator code passing a unit test is obtained; and submitting the target operator code and the target test code which pass the normativity check to a code warehouse through a code auditing engineer agent so as to automatically realize the whole-process unit test of the AI chip operator.

The invention discloses a software testing method and system based on a large model, and relates to the technical field of software testing, and the method comprises the following steps: step 1: describing a code review rule, and expanding specifications required to be checked: firstly, listing the check specifications required to be satisfied by a code, and according to a thinking chain and a zero- / feed-shot example, establishing a list of the check specifications required to be satisfied by the code; the check rules in the list are described as reasoning instructions of the model according to the check key points and the check steps through combination of one or more technologies of context learning; 2, code slicing: performing structure analysis and dependency analysis on the tested software project: firstly, slicing the code according to a function by utilizing a code analysis tool; according to the software testing method and system based on the large model, convenient project management, user-defined configuration and high-automation-degree test execution can be implemented on a software evaluation task, and the evaluation efficiency is effectively improved.

The invention provides a code review model training method, device and equipment, and a code review method, device and equipment, and the code review model training method comprises the steps: obtaining a training sample which comprises a sample code and review information of the sample code; a retrieval sub-model in the pre-training code review model is utilized to retrieve the sample code, multiple pieces of context information are obtained, and different pieces of context information correspond to different retrieval ranges; combining the plurality of pieces of context information with the sample code to obtain a plurality of pieces of prompt information; generating initial review information of the multiple pieces of prompt information by utilizing a generation sub-model in the pre-training code review model; selecting output review information from the multiple pieces of initial review information; and based on the difference between the output review information and the review information of the sample code, optimizing model parameters of the pre-trained code review model to obtain a trained code review model. The context understanding capability of the model can be enhanced, and the understanding of the model on complex codes is improved.

The invention provides an algorithm plug-in collaborative development method and system based on multi-version control, and relates to the technical field of software development, and the method comprises the following steps: creating a development task and distributing the development task to a plurality of development nodes, constructing a dependency graph and analyzing version conflicts, performing code review, and deploying and testing in test environments of different version underlying frameworks. The compatibility score is calculated through the support vector machine, the release version is determined, and the release information is synchronized, so that collaborative development of multiple development nodes is realized, dependency conflicts are recognized in advance, and the compatibility of the algorithm plug-in under a multi-version framework is ensured.

The invention relates to the field of automation, the field of artificial intelligence and the technical field of code quality management, and provides an automatic online code review method and system. The method comprises the following steps: compiling a self-defined rule description document, and compiling or updating a formulated coding specification document; a webhook request is set in a code warehouse in advance and used for automatically triggering an external service or process when a specific event occurs in the code warehouse; compiling or modifying codes, and manually / automatically submitting the codes to a code warehouse; the code warehouse automatically triggers a pre-configured webhook request, and then an interface of an AI code review system is called for code review; the AI review system pulls the to-be-reviewed code, performs rule matching on the to-be-reviewed code according to preset rule template configuration, detects whether a code violating a rule exists or not, and generates a review report after review is completed; wherein the rule template configuration comprises a rule description document and a coding specification document.

The invention provides a code optimization and reconstruction system which comprises a code analysis module used for analyzing the structure, behavior and operation performance of an initial source code of a program and preliminarily repairing the initial source code according to an analysis result to obtain a target source code; the demand and architecture analysis module is used for analyzing the consistency of the demand of the program and the architecture of the target source code and monitoring the demand change of the program; when inconsistency between the demand and the architecture is identified or the demand is changed, recommending a code architecture and a mode according to a consistency analysis result and the demand change; and the optimization and reconstruction module is used for analyzing potential defects of code modes, performance bottlenecks and code changes of the target source codes and generating optimization and reconstruction suggestions or automatically optimizing and reconstructing codes. According to the system, the manual code review time is shortened, and the code optimization and reconstruction efficiency is improved; manual intervention is reduced, and the code optimization and reconstruction accuracy is improved.

The invention discloses an intelligent review method and system applied to data processing. The method comprises the steps of obtaining historical review data; constructing a target review rule code library and a target review problem library according to the historical review data; obtaining a preset first model; training the first model based on the target review rule code library and the target review problem library to obtain a target code review model and a target operation monitoring model; reviewing a preset data processing algorithm through the target code reviewing model and the target operation monitoring model to obtain a target reviewing result; generating a target review report according to the target review result; and according to the target review report, updating contents in the target review rule code library and / or the target review problem library, and performing update training on the target code review model and the target operation monitoring model. By adopting the method and the device, the compliance of algorithm codes in the data processing and training process can be ensured, the data security is ensured, and data leakage is prevented.

The invention discloses a code review method, system and device, a storage medium and a program product, and the method comprises the steps: cleaning a to-be-reviewed code snippet, and obtaining an incremental code snippet and position information; vectorizing the incremental code snippets; semantic query is conducted on the vectorized incremental code snippet data based on an ES database according to the cosine law, development rules of similar semantics are obtained and ranked from high to low according to similarity values, and a preset number of first several snippets are obtained; recombining cue words, wherein the recombining cue words comprise incremental code snippets and a preset number of development rules; and sending the recombined cue word to a general generative large model to obtain an examination suggestion. The code snippets to be examined are pushed to the gilab warehouse by a developer, and are cleaned by the gilab warehouse through webhook triggering. According to the review method, the whole process is automatic, the review efficiency is high, review can be conducted according to the development rules in the team, knowledge sharing is achieved, the review result is objective, and the problem that the text exceeds the upper limit is avoided.

The invention provides an intelligent analysis method, system, equipment and program product for automatic code review, which are applied to the field of computer software engineering and development tools, and the method comprises the following steps: obtaining code difference data, preprocessing to generate a semantic code unit, constructing a structured cue word and inputting the structured cue word into a large language model, according to the scheme, the problems that in existing code review, semantic understanding is insufficient, and efficiency is low can be solved, automatic risk identification and analysis of code changes are achieved, and review efficiency and accuracy are improved.

A retrieval-augmented neural transformer model with chunk cross-attention predicts a code review given a proposed source code change, represented as a code diff hunk, and a set of historical code review comments. The code diff hunk represents proposed edits to a source code snippet with its surrounding context that has not been changed. The historical code review comments are associated with code edits that are semantically similar to the proposed source code changes. The code diff hunk is partitioned into chunks which are used to find semantically similar historical code review comments. The set of historical code review comments is aggregated and used to guide the model in makings its predictions.

The invention provides an intelligent code review method and system, and relates to the technical field of program testing, and the method comprises the steps: binding a triggering mechanism based on an item needing code review in a code management tool by a developer; submitted local codes are obtained, a background interface is automatically called according to a triggering mechanism, and the local codes comprise incremental codes or full-quantity codes submitted by a developer; if it is determined that the type of the local code is an incremental code, the local code is filtered, and submission information corresponding to a target code meeting a filtering condition is written into a cache to wait for a timed task to execute a review task; when the timed task is executed, the submission information is taken out from the cache, and the target code is split into single submission codes according to the submission information; scoring each submitted code to obtain code scores, and giving review suggestions for the submitted codes; and counting an average score of the plurality of code scores to obtain a comprehensive score corresponding to the target code. The code review efficiency can be improved.

In some implementations, a device may obtain an indication of a proposed change to a subset of executable code from a set of executable code. The device may determine, via one or more models, first context information associated with the proposed change and second context information associated with at least one of the subset of executable code, the set of executable code, or a context of the subset of executable code within the set of executable code. The device may determine a scrutiny level for review of the proposed change based on at least one of the first context information or the second context information. The device may obtain, via the one or more models, review information associated with the proposed change, wherein the one or more models apply the scrutiny level to obtain the review information. The device may perform an action based on the review information.

A method of performing code review and a code review system are provided. The code review system includes a code repository, a static scanning tool, an analytical neural network and a generative neural network. The code repository is configured to store an original source code and a new code created by a developer in response to a code change request to merge the new code with the original source code. The static scanning tool is configured to collect data associated with each commit in the new code. The analytical neural network is implemented with an analytical AI and configured to assess a risk level of each commit in the new code. The generative neural network is implemented with a generative AI and configured to provide a code summarization and an initial code review comment of each commit in the new code.

The invention relates to the technical field of software engineering, and discloses a software development code review system and method based on reinforcement learning, and the system comprises a historical code feature collection module, a software development code operation parameter collection module, a code feature matching optimization module, a code feature optimization analysis module, and a code review result output module. The method comprises the following steps: extracting code features in a historical code library according to a time sequence, collecting code operation parameters in a software development process in real time, completing evaluation on software development code operation, performing feature matching optimization on the code operation parameters based on an evaluation result, obtaining an evaluation result of a to-be-evaluated operation code after matching optimization, and performing evaluation on the to-be-evaluated operation code according to the evaluation result. Based on the initial evaluation result and the feature optimization analysis result of the software development code, a code review result is output, early warning information is provided for the client through initial evaluation and matching optimization evaluation, and the code quality and safety in the software development process are improved.

A system for evaluating a pull request process for a code repository is configured to access pull request data for a plurality of completed pull requests associated with code stored in a code repository and process the pull request data to identify pull requests with a policy pass / fail characteristic indicative of environmental error. The identified pull requests are then analyzed to determine which infrastructure and / or software component of a code review system is a source of the environmental error and / or a rate of occurrence of the policy pass / fail characteristic in the completed pull requests. An alert is then generated via a user interface of the pull request process evaluation system indicating an environmental error to indicate which infrastructure and / or software component of the code review system is a source of the environmental error and / or the rate of occurrence of the policy pass / fail characteristic in the completed pull requests.

A deep learning model is pre-trained with a large-scale of unsupervised data of code review tasks in order to learn the relationships between code changes and a code review. The pre-trained deep learning model predicts a code review given a code diff hunk in a code diff format. The code diff hunk includes the changed code and its surrounding context. The pre-trained deep learning model may then be fine-tuned with supervised data in order to make predictions for several code review activities, such as, code change quality estimation and code refinement.

The invention discloses a code review method and device based on a large model, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, and can overcome the defect of low efficiency of manual code review to a certain extent. According to the code review method based on the large model, code review is carried out based on the large model, and compared with manual review, the code review efficiency and accuracy can be improved by means of the excellent performance of the large model. Meanwhile, a JavaParser tool is used for analyzing the calling structure of the modified function, and a complete link diagram is constructed, so that the calling hierarchical relationship of each modified function in the whole project can be conveniently and clearly displayed, a large model can conveniently examine problematic codes of the process of the current function, and the efficiency of the process of the current function is improved. And therefore, the code review efficiency is further improved.

The invention relates to the technical field of code review, and provides a database code review method based on context enhancement and multi-dimensional confidence filtering, which comprises the following steps of: performing analysis and semantic enhancement processing on submitted code differences, acquiring context information related to current code change semantics by retrieving historical data, and storing the context information in a database; generating an enhanced review prompt; according to the enhanced review prompt, an artificial intelligence model is called for analysis to generate preliminary review suggestions, and a multi-dimensional evaluation system is adopted to evaluate each preliminary review suggestion to generate corresponding confidence evaluation features; and calculating a comprehensive score of each preliminary review suggestion according to the confidence evaluation features, implementing filtering and decision making according to the comprehensive scores, and outputting final review suggestions. According to the method, the accuracy and global correlation of the review opinions can be remarkably improved, the false alarm rate is reduced, the result credibility is enhanced, intelligent filtering is realized, and high-value feedback is output.

The invention relates to a programming review method and system based on multi-modal data. The method comprises the following steps that source code fragments are collected, character format matching is carried out, consistent items are output, and initial node mapping is generated; extracting a pointer reference and calling sequence, carrying out node association recombination, screening node levels, carrying out semantic mark matching, summarizing a connection path, checking a code security scanning index, and generating a dynamic semantic structure; carrying out stack-up matching to compare pixel distribution and label similarity, scanning edge coordinates, checking label corresponding positions and visible area distribution, judging feature co-occurrence, and generating multi-modal associated features; and performing program logic checking, analyzing a variable initialization sequence, generating a checking element set, comparing code security scanning indexes, generating security scanning information, judging a risk identification level and generating a review instruction list. Compared with the prior art, the information utilization rate and the correlation analysis depth in the code review process can be improved, and the accuracy of program defect and risk discovery is enhanced.

The invention provides a code review method and system based on a multi-dimensional knowledge graph and electronic equipment. The method comprises the following steps: constructing a business knowledge graph, a method calling graph and a code review knowledge graph; code data are obtained, a target code in the code data is determined based on the code review request, and the target code comprises code change content; generating a large model cue word in combination with the business knowledge graph, the method calling graph and the code review knowledge graph; and according to the big model cue word, calling an AI big model to perform joint review on the code change content, and generating a review report which at least comprises a repair suggestion. According to the method, multi-dimensional knowledge of business, code calling and code review is integrated, a more comprehensive and closely associated knowledge system is formed, the generated cue word can accurately guide the AI large model to review code change in combination with multi-field knowledge, blindness of the large model during review is avoided, and review efficiency and accuracy are improved.

The invention provides a software development system management method and system based on big data. A code review data set in remote collaboration software development is collected, a review knowledge graph is constructed based on time-space correlation characteristics of the data set, and graph nodes comprise review behavior modes correlated with geographical and time characteristics. Syntactic structure features of current code change are synchronously extracted, the syntactic structure features and historical review tracks in the knowledge graph are jointly coded, and a processing instruction containing version difference analysis priorities is generated. And determining an allocation strategy of the cross-regional review task by dynamically matching the processing instruction and the domain knowledge coverage range. And finally, according to a mapping relationship among the distribution strategy, the version priority and the quality evaluation node, generating an examination management regulation and control strategy adaptive to the software development system. According to the technical scheme provided by the invention, the management efficiency and precision of the software development system can be improved.

The invention particularly relates to a code writing, checking and modifying method based on a large model. According to the code writing, checking and modifying method based on the large model, a fine-tuning large language model is adopted to analyze user requirements, and multi-language initial codes are generated; executing deep code review through a static analysis engine, wherein the deep code review comprises syntax tree verification, security vulnerability mode matching and API dependency chain detection; executing the automatically generated test case set in an isolation environment, monitoring operation indexes in real time, and generating a defect thermodynamic diagram; and driving the large model to perform iterative code reconstruction based on a defect analysis result until it is confirmed that a preset quality standard is met. The code writing, checking and modifying method based on the large model is suitable for multiple languages, the defect rate of codes is reduced, the software development period is prolonged, the labor cost is reduced, and the vulnerability detection rate is greatly increased.

The invention belongs to the technical field of software code review, and discloses a code review comment automatic generation method based on multi-agent cooperation, which comprises the following steps of: 1, using agents to analyze missing important context of code change, and extracting code context from a code source file by calling an external syntax tree analysis tool; 2, a code change fragment containing a code context is handed over to a multi-agent framework, multiple agents inspect code changes from different dimensions, and it is ensured that inspection comments given by the agents cannot deviate from the dimensions to which the inspection comments belong by adopting an reflecting and cleaning unit; and 3, summarizing a plurality of comments from multiple agents, filtering low-quality suggestions in the comments, rearranging the suggestions according to dimension priorities, and finally outputting the processed comments. According to the multi-agent cooperation mode, the defects of single-agent examination can be effectively overcome, potential problems existing in codes can be found more comprehensively, and more valuable feedback information is provided for developers.

The invention discloses an AI code review method, system and device based on browser extension, and the method specifically comprises the steps: building a local term library and a review rule library through browser extension, persistently storing project specific terms and coding specifications, and forming a domain knowledge graph; when it is detected that the Gitlab MR page is loaded, capturing a developer cursor track and a window focus area in real time, and generating an operation thermodynamic distribution diagram; based on the operation thermodynamic distribution diagram, dynamically activating examination rule nodes of associated code blocks in the domain knowledge graph, and obtaining a rule node priority sequence; and according to the rule node priority sequence, performing static rule review on the high-heat region, and performing semantic defect detection on the correlation function chain to obtain a review result. According to the method, the problems that an existing tool is limited in deployment permission and lacks flexible self-definition capability can be solved, the user experience can be improved by optimizing an interaction mode, and a more efficient and convenient code review solution is provided for developers.