497 results about "Codebase" patented technology

A user query for information regarding data of a codebase is answered by a large language model given a prompt that includes examples of code segments from the codebase that are similar to the user query. The code segments from the codebase are associated with metadata that includes both natural language text and source code. The search for the examples of code segments from the codebase is based on embeddings of code segments and associated metadata that are closely similar to an embedding of the user query and context.

The invention provides a cross-frame code migration method and system, electronic equipment and a storage medium, and the code migration method comprises the steps: carrying out the frame-level conversion of a first frame code, and generating a target engineering skeleton adaptive to a second frame protocol; analyzing the construction configuration and the entry file, determining an entry point and a compiling rule of code migration, analyzing the first frame code through an abstract syntax tree, and generating a target code range list needing to be migrated; and according to the target code range list and the dependency item list, applying a preset conversion rule to the first framework code, and generating a second framework code adaptive to a second framework protocol. Through automatic framework-level conversion and dynamic rule adaptation, rapid migration from the first framework code to the second framework code is realized, a multi-terminal code library is unified, the manpower development cost is reduced, synchronous operation of business iteration and migration is supported, and the development efficiency is improved.

The embodiment of the invention discloses a code defect prediction method, a readable storage medium and a computer program product. The method comprises the following steps: constructing a plurality of prediction models and a plurality of agents; when the code library is updated, determining a plurality of update items, and obtaining new version codes of the plurality of update items; obtaining potential code defect types and potential code defect descriptions of a plurality of update items based on the new version code and the plurality of prediction models; generating defect reports of a plurality of update items based on the potential code defect types, the potential code defect descriptions and the plurality of agents; and carrying out authenticity verification on the defect report, and repairing the new version code after the authenticity verification is passed. According to the method, code defects can be efficiently and accurately predicted, and a defect report is given for repairing related defects, so that software research and development are facilitated.

The invention belongs to but is not limited to the technical field of software development, and particularly relates to a multi-round reasoning and feedback optimization method and system for industrial software codes, and the method comprises the steps: S1, vector code library establishment: the establishment of a vector code library is composed of three parts of feedback establishment, vector database establishment and format summarization; s2, code reasoning optimization: the code reasoning optimization mainly comprises four aspects: control flow graph analysis, multi-query generation and reciprocal ranking fusion retrieval, reasoning generation subtask and code chain submodule generation, and code combination completion; and S3, feedback optimization: the feedback optimization is composed of score feedback optimization and log feedback optimization. According to the method, an automatic verification mechanism is introduced, logic consistency check and performance evaluation are carried out on the generated and optimized codes, and the correctness and execution efficiency of the codes are ensured. The verification mechanism can find and correct errors in real time in each stage of generation, so that the cost of manual verification is reduced, and the development efficiency is improved.

Targeted dynamic analysis of code is provided by utilizing static analysis and dynamic analysis. A static code analysis system can operate to generate an initial analysis of code. The results of the initial analysis may be used to target a dynamic analysis system to analyze portions of code identified by the static analysis as potentially containing defects. In some cases, a preliminary dynamic analysis may be used to generate code inputs that may be utilized in future analyses to determine the inputs necessary to target particular code paths identified by a static analysis as potentially defective. Future updates to the code base may utilize the generated data to target analysis to code paths affected by the updates.

Systems, methods, and devices are provided to intelligently identify run-time complications within a codebase and provide recommended solutions for the identified run-time complications. A behavioral model of the codebase is accessed that depicts a run-time behavior of the codebase. The behavioral model generated is then interrogated to predict run-time complications within the codebase. Recommended solutions are generated and provided in a user interface for resolving the run-time complications.

Systems and methods for resolving code vulnerabilities through collaborative agents which may include accessing a code base of an identified vulnerability; configuring a plurality of autonomous agents, each comprising a predefined agent role associated with application security remediation process; executing a directed workflow of the plurality of agents, wherein the workflow is a conditional sequence of agent-driven processing steps for generating a proposed resolution to the identified vulnerability; and outputting a candidate resolution for the vulnerability based on results produced by the workflow.

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.

Systems and processes are disclosed for enhancing cybersecurity and optimizing software repositories through integration of web crawling, web scraping, feature engineering, and advanced machine learning algorithms to detect phishing attempts, prevent account takeover fraud, and identify unused code in repositories. The system collects and refines data from various sources, including transaction logs, customer databases, device details, external data sources, and historical fraud data, to build comprehensive datasets. Feature engineering creates new, meaningful features from the refined data, which are used to train and evaluate machine learning models. The best-performing models are deployed in production to monitor incoming communications and transactions in real-time, flagging suspicious activities and optimizing codebases. This processing ensures timely detection and prevention of security threats while maintaining efficient software development processes. Robust protection is provided against evolving cyber threats and enhances software performance and security through continuous learning and adaptation.

The invention discloses a programming assistant framework, a programming assisting method, equipment, a medium and a product, and relates to the technical field of artificial intelligence, and the programming assistant framework comprises a DevOps platform integration module, a DevOps platform integration module, a DevOps platform integration module, a DevOps platform integration module, a DevOps platform integration module and a DevOps platform integration module, wherein the DevOps platform integration module is used for receiving input information sent by any link in links for code development and maintenance; the input information is sent to a code completion and dialogue function module; the code completion and dialogue function module is used for acquiring code related problems in input information, reading source codes in a project code library, and performing abstract syntax tree analysis on the source codes to obtain an abstract syntax tree; and the RAG retrieval enhancement generation module is used for performing semantic recognition on code related problems to obtain a user intention, traversing the abstract syntax tree according to the user intention, determining code context information, obtaining retrieval information according to the user intention and the code context information, and outputting code suggestions generated according to the retrieval information. The overall development efficiency in the development process is improved.

The invention discloses an AI-based low-code automatic development method and system and a medium, mainly relates to the technical field of image processing, and is used for solving the existing problems that the demand conversion efficiency is low, the code quality depends on manpower, and the complex scene support is insufficient. Comprising the steps of extracting a current functional entity corresponding to a code function core feature; based on the current functional entity and the historical functional entity, extracting an associated sub-graph containing the current functional entity and the historical functional entity from a preset knowledge graph corresponding to the demand technical field; dSL grammar corresponding to the demand technical field is obtained, entity nodes related to the associated sub-atlas, inter-node interaction logic rules and code function core features are input into the DSL grammar, and intermediate representation IR is obtained; and according to the intermediate representation IR, the code function core features and the current function entity, a model trained by a large-scale code library is utilized to generate a specific code.

The invention discloses a retrieval enhanced warehouse-level code completion method based on semantic topology and disambiguation redundancy pruning. Firstly, a key value code library for accurate mapping of source code fragments and induction sub-graphs is constructed through a specific slicing algorithm; in the retrieval stage, four-level layered optimization is adopted, structural similarity is evaluated by extracting a deep semantic relationship, trimming accurate duplicate items and using a new graph-based measurement (tradeoff is performed on editing according to topological importance), results are reordered to maximize correlation and diversity, candidate items are systematically refined, and the retrieval efficiency is improved. The problems of retrieval redundancy solidification and surface similarity misleading are solved, meanwhile, cross-module dependence is analyzed through an external perception identifier disambiguator, and the problem of cross-file symbol ambiguity is solved; and finally, fusing an optimization result to generate a prompt to drive the LLM to generate higher-quality output. According to the invention, through coordination of semantic and structural signals, strong performance can be obtained even in a large-scale and resource-limited code library. Meanwhile, the design allows it to be orthogonally complementary to other cross-file methods, providing collaborative improvements when used in combination.

The invention discloses a code translation method and device based on a large language model, and the method comprises the steps: firstly executing an architecture understanding workflow, and forming an architecture design document O1 of a code library; according to the architecture design document O1 of the code library, in combination with the demand document P1, code translation task decomposition workflow is executed, and a task planning list O2 is formed; elements in the task planning list O2 are sequentially extracted, and the code debugging workflow is executed to complete code translation and debugging work. A warehouse-level code architecture understanding workflow, a code translation task planning workflow and a code debugging workflow are constructed based on a large language model (LLM), and the workflows are organically combined to realize automatic translation of warehouse-level codes. According to the method, the limitation of function level translation in the prior art can be broken through, automatic translation of warehouse level codes is realized, the development cost is greatly reduced, and the translation efficiency is improved.

The invention relates to a code diagnosis method, system and equipment, and the method comprises the steps: carrying out the preprocessing of code data, and generating structured data; vectorizing the structured data to construct a vector database; based on a user diagnosis request, a retrieval enhancement generation technology is adopted to retrieve related context fragments; screening out target context fragments, combining the target context fragments into context information, and outputting the context information to a large language model; the large language model identifies potential problems in the code data and generates repair suggestions. According to the method, efficient retrieval can be performed in a large-scale code library on the basis of the large language model by combining and applying a retrieval enhancement generation technology, necessary and accurate context information related to a user diagnosis request is provided for the large language model, so that the large language model can identify potential problems in codes more accurately; and a targeted repair suggestion is automatically generated.

The invention relates to the field of wisdom education, and particularly discloses a teaching strategy optimization model and grammar error early warning method based on data mining, and the method comprises the following steps: S1, obtaining original code data in a student code library in real time, and preprocessing the original code data to obtain a structured data matrix; s2, for the structured data matrix, generating a grammar parse tree through a grammar parser, positioning error nodes and extracting context features by using a node traversal algorithm, and constructing a single-error multi-dimensional feature vector; meanwhile, an error association rule base is constructed based on historical error data, a causal relationship across error types is identified, and dynamic knowledge graph data is formed. According to the technical scheme, association analysis and deep mining can be carried out on the multi-source learning data, dynamic solution suggestions for student individual errors or group generality errors are formed, and teachers are assisted to quickly adapt to dynamically changing learning requirements of the students.

The invention discloses a software development management method based on big data, and relates to the technical field of software development management, and the method comprises the steps: collecting multi-source development data, transmitting the multi-source development data to a data lake in real time through a distributed message queue, and generating a structured data stream with a timestamp and a time sequence relation graph; performing Monte Carlo sampling on the three-dimensional radiation field model of the neural radiation field and the causal influence graph, simulating the cascade influence submitted by the current code, and outputting a risk value and a dependency chain; loading a neural radiation field three-dimensional code space model, a node risk value and a dependency chain, dynamically rendering a causal influence path, and generating a visual risk report; and generating a code optimization task according to the visual risk report, submitting an optimized code version to a code library, and calibrating parameters of the neural radiation field three-dimensional code space model. According to the method, the spatio-temporal characteristics of code change are analyzed, Monte Carlo simulation is combined, hidden dependence and a risk conduction chain are identified, and the comprehensiveness and predictive ability of framework corruption analysis are 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 relates to the technical field of automatic testing, and discloses an automatic test case generation method and device and a medium, and the method comprises the steps: constructing test case knowledge bases of different fields, and carrying out dynamic and static joint feature analysis on code libraries corresponding to field test data; performing intention recognition on the test demand data, and performing atomization operation on the test demand data according to the test demand intention; identifying a test demand code block associated with the test demand data; generating a basic test case of the test demand code block according to the atomization parameter, and injecting an abnormal stream into the basic test case according to the atomization parameter to obtain a target test case; and selecting a similar test case corresponding to the target test case from the test case knowledge base, performing regression test on the test demand code block by using the regression test case, and optimizing the regression test case according to a test result to obtain an automatic test case. According to the invention, the coverage rate during test case generation can be improved.

Technology for assisting software developers in searching, understanding, and identifying particular portions of codebases is provided herein. With the given tools, a user can submit a natural language query. The system will analyze the query and identify relevant portions of the codebase using a previously generated behavioral map of the codebase. Once relevant portions of the codebase are identified, the system generates a prompt for submission to a foundation model to elicit a response to the user's query. The response provided to the user will be complete and relevant to the user's particular codebase.

The invention discloses an equipment test model code generation method based on a large model, and the method comprises the steps: designing an equipment digital test demand analysis module which is used for constructing an equipment digital test identification framework system through combining demand description to extract a digital test demand generated by an equipment test model modeling code; designing a large model training code collecting and processing module, and generating a high-quality code data set; the design equipment test model code library module is used for carrying out structured storage on the high-quality code data set, constructing a knowledge graph based on structured data and designing a vectorization dynamic query function; and designing a large model pre-training and fine tuning module, and performing secondary pre-training and fine tuning on the base code large model by using the high-quality code set and a vectorization dynamic query function to generate a final equipment test model code. According to the method, the code generation speed and accuracy are remarkably improved, and it is ensured that the generated code has wide adaptability and universality.

A computerized method generates a structure configuration from a codebase and generates application code using the structure configuration. Code examples of the codebase are obtained, and a structure configuration is generated using the obtained code examples and a standard configuration dataset. The structure configuration includes code components specific to the codebase. Labels that are indicative of component attributes are assigned to the code components of the structure configuration. A code generation request is received, and the request is converted into a plurality of feature prompts. At least one code component is mapped to each feature prompt based on semantic similarity of the feature prompt to a label of the mapped code component. Application code is generated using the mapped code components. The generated application code is deployed for execution. This method enables the automatic generation of code that conforms to common patterns and accurately exhibits the features requested.

An online system performs inference requests in conjunction with the model serving system to perform AI (text-based LLM or multi-modal transformer)-generated integration test variants. Instead of rigid code-specified integration tests, the LLM creates integration test variants that follow a specification. Given one or more files from the codebase of an application and a specification for integration testing, the LLM compiles an integration test including a series of actions (e.g., API calls) as runnable code and assertions about the state of the application after the actions are executed.

A codebase system that maintains and utilizes a dynamic mapping between a collection of tests and files within a codebase to ensure error detection and code validity. The codebase system accesses a collection of tests configured to process files within the codebase to identify errors. It then generates a mapping by executing these tests to determine the specific files each test operates on. Upon detecting updates to the test collection, the codebase system updates this mapping by re-executing the updated tests, identifying the new set of files for each test, and aggregating these updates into the existing mapping. When changes to the files in the codebase are detected, the codebase system accesses the updated mapping to identify which tests relate to the altered files. The codebase system then executes this relevant subset of tests to validate the modifications made to the files.

The invention discloses a video acquisition method and device based on modular low coupling, and relates to the technical field of video processing. The method comprises the following steps: initializing a video processing system, creating a unified system resource management interface, and realizing centralized management of video acquisition, encoding, decoding, OSD control and NPU push modules; based on a modular design principle, video acquisition, coding, decoding, OSD control and NPU push functions are packaged into independent modules, and data interaction between the modules is realized through a standardized interface. The video processing system is modularly designed, and a unified standardized interface is provided, so that the effect of greatly reducing project code coupling is achieved, a developer does not need to pay attention to underlying logic when calling the interface, and module function independence is guaranteed; by means of the modular interface design, developers can rapidly integrate platform functions, development work can be completed without deeply knowing the whole code library, and therefore the project development efficiency is improved.

Intelligent code block selection and codebase updating using generative AI is disclosed herein. A user may request a code block for performing a task based on a given quality parameter (e.g., most energy efficient, fastest, or the like). The system may select an AI model for evaluating code blocks to meet the quality parameter. The system may identify code blocks for evaluation and execute each code block in an isolated testing environment. The selected AI model evaluates each code block execution and selects a code block based on completing the task in a way that most adheres to the quality parameter. The selected code block is returned via a user interface. The selected code block may be stored in a configuration code building block library associated with the quality parameter and the task and used when developing and revising software for the industrial automation environment.

The invention relates to a method and equipment for generating a code map for a project and a storage medium, and belongs to the technical field of software engineering. The method comprises the following steps: segmenting a code file in a code library into code snippets, generating a corresponding vector representation for each code snippet, and storing the code snippets and the vector representations thereof into a vector database; extracting a project structure tree, and adopting a large language model to generate a UML generation plan according to the project structure tree; generating a UML graph of the current project by adopting a large language model based on the UML generation plan and codes in the code library; displaying the UML diagram of the current item on a graphic interaction interface through a rendering engine; and responding to an instruction that a user clicks a selected node in the global UML graph, returning a code snippet related to the selected node, and generating a detailed view related to the selected node. On the basis of the method, the UML diagram of the whole project can be generated through one key, and the relation between the complete structure and the function module of the project can be quickly and visually known.

The invention discloses a stain analysis and learning model combined static source code scanning method, and belongs to the technical field of source code scanning. Comprising the following steps: acquiring source codes in a project code library to establish a source code set, and analyzing the source codes in the source code set into an abstract syntax tree and code features in the abstract syntax tree; obtaining source codes in the source code set, public data of the vulnerability database and historical vulnerability code samples in the enterprise code library to build a data set, building a deep learning model, standardizing data in the data set to obtain marked data, forming a training set by the data subjected to optimization training of the deep learning model, and storing the training set in the enterprise code library; training data in the optimization model are collected to serve as a verification set to evaluate the optimization model, model analysis results are obtained, an analysis report is obtained for the two analysis results, specific vulnerability types are recognized, and repair suggestions and codes are provided. According to the method, potential security vulnerabilities can be identified, detailed repair suggestions can be provided, and developers are helped to quickly solve security problems.

The invention discloses a vulnerability detection method and system based on code similarity analysis, and the method comprises the steps: obtaining patches in an open source project warehouse to generate vulnerability codes, and constructing a vulnerability code library; analyzing the vulnerability code library and the source code library of the target program, and respectively generating abstract syntax tree sets of the vulnerability code library and the source code library of the target program according to the generation rules of the abstract syntax trees; abstracting the abstract syntax tree set through a conversion rule to obtain a new abstract syntax tree set; key values and mark strings are extracted from the abstract syntax tree of each function to serve as code features, the mark strings are converted into hash values, a vulnerability dictionary and a target dictionary are obtained, similarity is calculated, whether a target program has reuse of vulnerability codes or not is judged, and a result is output. According to the method, similarity comparison of codes is converted into comparison of key values and hash values, and security vulnerabilities of large-scale software programs can be rapidly detected on the premise of ensuring high accuracy.

The invention provides a retrieval enhancement code generation method and system based on retrieval query statements. The method comprises the following steps: acquiring a plurality of retrieval query statements; screening out context information matched with the natural language requirement and the retrieval query statement from the code library; generating a first code segment according to the natural language requirement and the context information; through a plurality of first code segments in one-to-one correspondence with the plurality of retrieval query statements and a corresponding label of each first code segment, training and optimizing a retrieval query statement generator to obtain a target retrieval query statement generator; based on a target natural language requirement, utilizing a trained and optimized target retrieval query statement generator to generate a query statement; and using the query statement to retrieve the related code context, and inputting the target natural language demand and the retrieved code context into the code generation model to complete the code generation of retrieval enhancement. According to the embodiment, the interference of redundancy and irrelevant content can be effectively reduced, so that the code generation quality is improved.

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.