135 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 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 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 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 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 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 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.

An Artificial Intelligence (AI) driven pull request summarization system utilizes a large language model to classify the code changes of a pull request and to generate a summary of the changes contained in the pull request. The large language model predicts the pull request classification and summarization given a prompt that includes the top-k code changes in the pull request of a repository. The code changes are prioritized based on the most impact that a code change has on the files, methods and classes in the repository. Each of the top-k code changes is linked to a related open issue of the repository, if any. A suggested code reviewer for a code change is then selected from an author or commentator associated with the linked open issue.

Cloud computing architecture is described for implementing test modules in a communication-controlled cloud environment with access to private data. The test modules perform synchronous tests on the private data and export test results to an analytic environment subject to data export policies. An analytic application is used to asynchronously analyze the test results in the analytic environment. The cloud computing architecture alternatively or additionally includes an interface for deploying investigation-bound cloud environments in restricted subnets. A collection of software is instantiated in the investigation-bound cloud environment, and the investigation-bound cloud environment may be accessed with remote access credentials using a remote access protocol for testing the collection of software. Information about the investigation-bound cloud environment displayed in the analytic application, and the analytic application and the restricted subnet are forcibly deleted when the investigation is complete.

The invention provides a vulnerability discovery method and system based on symbol-semantic hybrid reasoning, and belongs to the technical field of software security. The method comprises the steps that a target program is analyzed, and an event sequence is extracted; converting the event into a symbol with a time sequence label and confidence, and constructing a symbol dependency graph; reasoning based on the dependency graph, calling a large language model to generate a semantic reasoning action when the certainty is insufficient, and updating the state after symbol verification; new rules are extracted from the inference chain passing verification through reinforcement learning, and self-evolution is achieved; and outputting a vulnerability report containing the reasoning path and the confidence coefficient. The system correspondingly comprises a time sequence probability symbol module, a symbol-semantic bidirectional coupling reasoning module, a reinforcement learning self-evolution module and a report generation module. According to the method, the preciseness of symbol logic and the generalization ability of a semantic model are fused, the accuracy, interpretability and adaptive ability of vulnerability detection are effectively improved, and the method is suitable for security audit and code review of a complex software system.

The invention discloses a code review assisting method and system based on a large model. The system is in butt joint with a code review platform, and after a patch set change event is received, codes are detected out and engineering snapshots are constructed. And the LLM agent interacts with the large language model, calls an LSP client to obtain diagnosis and call chain information when static analysis is needed, and feeds an observation result back to the model to generate an examination conclusion. And when the LSP returns an error, a negative feedback mechanism is triggered, the model is guided to correct parameters and symbols, and illusion is reduced. And the review conclusion is converted into comments and automatically published to the platform, so that high-risk change automatic identification is realized, the manual workload is reduced and the accuracy is improved.

The invention discloses a real-time code review processing method and system and related equipment, relates to the technical field of code review, and aims at obtaining model context protocol link information in real time, enabling a machine learning model to better understand the intention and context relationship of codes and improving the efficiency of code review. Information such as model context protocol link information, code intention and context relation is input into a machine learning model to be analyzed, so that the specific data performance, the overall code structure and the specific function of the code in a network are dynamically understood; potential problems and optimization points are identified on the basis of understanding of specific data performance, overall code structure and specific functions of codes in a network, and dynamic analysis and model improvement can be performed according to factors such as specific functions and calling paths of the codes on the basis of a model context protocol, so that comprehensiveness and accuracy of code review are improved.

The invention discloses a code repair model training method and a code repair method and device.The code repair model training method comprises the steps that an execution code corresponding to historical code review information is obtained, the execution code comprises a historical review code block corresponding to the historical code review information in the source code and an execution dependency code of the historical review code block; determining candidate execution codes capable of reproducing the historical code review information based on a review source of the historical code review information and the execution codes; determining a training sample based on the candidate execution code; and performing model training based on the training sample to obtain a code repair model. Therefore, the review code block corresponding to the review information and the execution dependency code can be obtained from the source code, so that the code review information can be reproduced by the extracted execution code, the accuracy of the training sample is ensured, the code quantity of the candidate execution code is reduced, and the training efficiency is improved.

The invention relates to the technical field of computers, in particular to a code review method and device, electronic equipment and a storage medium, which are used for efficiently screening out high-quality and high-diversity training data so as to improve the accuracy of code review. The method comprises the following steps: acquiring a training sample set and a test sample set, and dividing the training sample set into a plurality of training sample subsets; the test sample set comprises test samples reaching expected quality; after a to-be-trained code review model is preliminarily trained based on the training sample set, the test influence degree of each training sample subset on the code review model on the test sample set is determined; for at least one target training sample subset of which the test influence degree meets a preset influence condition in the plurality of training sample subsets, performing clustering according to a sample gradient to obtain a plurality of clusters; and sampling is carried out in each cluster, and the code review model after preliminary training is trained again according to the obtained sampling sample set.

The invention discloses an AI-based code quality and defect analysis method and device, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a code merging request submitted by a user; in response to the code merging request, extracting code change content based on the code change version; inputting the code change content into a pre-trained large model, and performing semantic analysis on the code change content by the large model to detect potential quality problems in the code change content; for each potential quality problem, obtaining related code context information of the potential quality problem, inputting the related code context information into a pre-trained large model, and performing authenticity verification by the large model based on the related code context information to screen out a real quality problem; and according to the real quality problem, generating a code review report. According to the method, efficient, objective and continuous code quality review can be realized on the premise of ensuring the review quality.

Given prompts including code changes, association intent, and extended context made to a source code program, a code review is automatically generated by a large language model. From the perspective of the code reviewer, the intent represents the problem of code changes and is predicted from a neural classifier given code changes in a code difference format. The neural classifier is a neural encoder transformer model that is pre-trained on various code review datasets and fine-tuned on code disparity blocks of code changes tagged with intent.

The embodiment of the invention provides a code specification review method and device based on the combination of a large language model and a static code check tool and electronic equipment, and relates to the field of code specification review. Each code specification rule is read and understood; the type of each code specification rule is analyzed, a proper checking tool is distributed to each code specification rule, and the checking tools comprise a large language model and a static code checking tool; generating a configuration file of the static code checking tool according to the code specification rule type, and dynamically adjusting parameters of the configuration file according to the specific condition of the project; scheduling a large language model or a static code checking tool for checking by utilizing an MCP architecture; and summarizing all inspection results to generate a structured code review report. The method solves the problems that a traditional code review tool is low in execution efficiency and lacks intelligent judgment.

The application discloses a code review method and related equipment, and relates to the technical field of software development. The method comprises the following steps: if a review triggering event for target code is detected, obtaining context information of the target code; based on the context information, calling a preset large language model to perform primary code review on the target code, and generating a primary review result; if the primary review result meets a preset condition, obtaining user feedback information for the primary review result; based on the context information, the primary review result and the user feedback information, calling the preset large language model to perform secondary code review on the target code, and generating a target review result. The application introduces a large language model primary review based on context and a secondary review mechanism combined with user feedback in the code review process, realizes the automation and iterative optimization of code review, reduces the dependence on manual work, improves the review efficiency, and improves the accuracy and reliability of the review result.

A code review comment is automatically generated using multiple agents that perform a dedicated task using a particular language model. A code quality estimator agent uses a code quality encoder model to determine whether a code change to a file of a repository presents a risk to the repository if merged. For those code changes classified as presenting a risk, a comment generator agent uses a generative language model to generate an initial code review comment for the code change and determines a severity of the issue with the code change. A comment critic agent uses a reasoning language model to critique the initial code review comment generated by the generative language model. A final code review comment is output by the comment critic agent when the comment critic agent determines that the initial code review comment is satisfactory.