173 results about "Code review" patented technology

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 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 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 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.

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.

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.

Embodiments of the invention disclose a code review method and apparatus, an electronic device and a storage medium, and can solve the problem of how to effectively improve the accuracy and efficiency of code review. The method comprises the steps of obtaining a to-be-reviewed code, wherein the to-be-reviewed code comprises code content and a review instruction; in a pre-stored knowledge base, retrieving the code content to obtain target context information; obtaining target input content according to the code content and the target context information; and inputting the target input content and the review instruction into a preset large language model to obtain a target review result.

The invention discloses a code review comment generation method and system based on reinforcement learning, belongs to the technical field of crossing of software engineering and artificial intelligence, and aims to solve the technical problem of how to improve the accuracy, efficiency and expandability of code review and overcome the defects that an existing tool is low in comment quality and poor in practicability. According to the technical scheme, the method comprises the steps of collecting and preprocessing data of code differences, human comments and real correction codes, and constructing a data set; code review comments are generated, specifically, a pre-trained large language model LLM is finely adjusted through the data set, code difference data are collected, and the review comments are obtained; semantic similarity reward: calculating the semantic similarity between the generated comment and the real comment, and generating a reward signal Rsematic; the generated comment and code difference is input into a code optimization model, a corrected code patch is generated, the similarity between the generated patch and a real patch is evaluated, and a reward signal Rtask is generated; performing reinforcement learning and fine tuning on the large language model LLM; and deploying and generating comments.

The invention relates to the technical field of infrastructure operation and maintenance, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a code review method, device and equipment based on knowledge base assistance and a medium. Constructing a domain knowledge base based on domain proper nouns and code entity naming specifications; constructing an architecture knowledge base based on the code framework specification; obtaining a to-be-reviewed code change; component calling, naming specifications and structure specifications in the to-be-examined code change are examined, and corresponding examination results are generated; and generating a code review report according to the check result. By constructing the component knowledge base, the field knowledge base and the architecture knowledge base, multi-dimensional joint inspection of code changes from component calling and naming specifications to architecture specifications is achieved, the automation level of code inspection can be improved, manual dependence is reduced, and code quality and normalization are enhanced.

The invention discloses a code version control method and device based on an alliance chain, equipment and a medium, and relates to the technical field of code version control, and the method comprises the steps: uploading a corresponding new code file version to a preset distributed code storage system after a target code submission request is received, enabling the distributed code storage system to submit a hash value for the content generation code; generating a code review task based on the code submission hash value, and performing review through an alliance chain network; whether submission is approved or not is judged according to the review result, and after submission is judged to be approved, packaging is carried out based on the review result and the code submission hash value, so that a newly-added candidate block is generated; proposing the newly added candidate block to other nodes based on a consensus protocol, so that the other nodes can perform verification; and judging whether verification is passed or not according to a verification result, linking the newly-added candidate block to the alliance chain network after judging that the verification is passed, and performing code submission recording for the target code submission request.

The embodiment of the invention provides a code review method and a computer program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: analyzing a to-be-reviewed code into an abstract syntax tree, extracting a dependency relationship of each code node from the abstract syntax tree, calculating the code complexity of each code node, generating a topological structure diagram according to each code node and each dependency relationship, marking each network node according to each code complexity and a preset code rule, and generating a topological structure diagram according to each network node. And performing visual review on the to-be-reviewed code. Therefore, the cognitive load and troubleshooting time of a developer for positioning the architecture level problem in a large-scale project are effectively reduced and shortened.

The invention relates to the technical field of data processing and the field of insurance businesses and smart medical treatment, and provides a code review method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining code change information; performing context-aware retrieval processing on the code change information based on the code multi-dimensional knowledge base to obtain code context dependence information; checking the code change information and the code context dependence information based on a code checking module to obtain initial code comment information; screening the initial code comment information based on a preset value filtering module to obtain screened code comment information; performing adoption evaluation processing on the screened code comment information to obtain code correction evaluation information; and performing optimization processing on the code knowledge graph, the code checking module and the value filtering module according to the code correction evaluation information. Through the technical scheme, the code can be intelligently reviewed and processed, and the code review efficiency is improved.

The invention provides an AI-based code review opinion automatic classification and priority ranking method. The method comprises the steps of collecting code review historical data; data preprocessing: preprocessing the collected data, and constructing a training data set; an AI model is selected and trained; inputting a new review opinion; automatic classification: the model automatically classifies the review suggestions, and classification categories include but are not limited to code errors, code style problems, performance optimization suggestions and potential safety hazard prompts; performing priority ranking: performing priority ranking on the review opinions based on a preset priority rule and the classification result; and generating result output. According to the code review method and device, automatic classification and priority ranking of the code review opinions are achieved through the AI model, a traditional manual processing mode is replaced, the problems that manual classification is low in efficiency and priority ranking is high in subjectivity are effectively solved, and the overall efficiency and result accuracy of code review are improved.

The invention discloses a code review method and device, electronic equipment and a storage medium. Comprising the steps that a full-amount source code file of a target project is obtained from a code repository according to a code change event, multiple programming languages and dependency information corresponding to the programming languages are determined based on the full-amount source code file, and each source code file in full-amount source codes corresponds to one programming language; determining at least one to-be-reviewed source code file in the plurality of source code files, and extracting annotation information in each to-be-reviewed source code file; determining a cue word template corresponding to the to-be-reviewed source code file, and determining cue words of the to-be-reviewed source code file based on the programming language, the dependency information, the annotation information and the cue word template corresponding to the to-be-reviewed source code file; and processing each to-be-reviewed source code file and the corresponding cue word based on a preset large language model to obtain a code review result corresponding to each to-be-reviewed source code file. Flexible construction of the cue word is realized, and the accuracy of code review can be improved.

The invention provides a code review method and device and electronic equipment, and the method comprises the steps: obtaining a difference code between a first code and a second code, and obtaining a code suggestion for the difference code, the second code being a code obtained after the first code is modified; by means of the code suggestion and the difference code, an editing selection area and an expansion area of the editing selection area are determined in the second code, and the expansion area comprises the context of the editing selection area in the second code; and constructing a second prompt instruction by using the editing selection area, the extension area, the code suggestion and a preset rule, providing the second prompt instruction to a second large language model, and obtaining an output result of the second large language model, the output result comprising a confidence result of a third code obtained by the second large language model based on the preset rule, the third code is the code obtained after the selected area is modified and edited through the code suggestion. The code review efficiency can be improved.

The invention belongs to the technical field of software development, and particularly provides a research and development-oriented long and short-term memory framework construction method and system, which adopts a layered memory architecture to construct four core modules including a short-term memory compressor, a medium-term memory aggregator, a long-term memory graph and a cross-layer memory router. The system takes multi-source input such as research and development dialogues, code snippets and project documents as a starting point, extracts research and development elements through semantic analysis and entity recognition technologies, compresses lengthy dialogues into structured short-term memory by utilizing an attention distillation mechanism, upgrades high-frequency short-term memory into medium-term knowledge fragments based on a time sequence attenuation algorithm, and improves the research and development efficiency. And constructing a long-term knowledge graph containing developer portraits, project dependence and normative standards by adopting a graph convolutional network. Context understanding accuracy, multi-round dialogue continuity and personalized service quality of a large model in a research and development scene are remarkably improved, and the method is suitable for mainstream research and development tool scenes such as IDE plug-ins, code review and architecture design.