407 results about "Codebase" patented technology

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.

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 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 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 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.

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 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.

The invention belongs to the technical field of software engineering, and particularly relates to a unit test method for generating a discrimination model based on retrieval. Searching a to-be-tested function, namely a test case pair, and establishing a code library; constructing an efficient code retriever based on code embedding and mixed feature extraction; constructing a multi-input matching discriminator network, preparing an own data set, and training the network; integrating the code library, the retriever and the discriminator into a local knowledge base, and performing fine adjustment on the large model based on the local knowledge base to obtain a retrieval generation discrimination model; and generating a test case based on the retrieval generation discrimination model, and testing. According to the method, the discrimination model is generated by using retrieval, corresponding change is automatically carried out according to the previous test case, and the test with higher coverage rate and quicker speed can be realized.

Some examples of the present disclosure relate to risk analysis of test failures that occurred during a testing phase of a continuous integration pipeline. In one particular example, a system can detect a failure of a test during a testing phase of a continuous integration pipeline. The testing phase can involve executing a group of tests in a plurality of configurations prior to merging an update to source code into a code base of a software application. Based on detecting the failure of the test during the testing phase, the system can access historical data associated with prior executions of the test, determine a risk score associated with the failure of the test based on the historical data, and perform an action associated with the source code based on the risk score.

The invention discloses a large language model reasoning acceleration method and device based on two-stage speculative decoding and a storage medium, and the method comprises the steps: constructing and initializing a Trie tree, and inserting a historical corpus, and phrase sequences in a document library or a code library into the Trie tree one by one; in the reasoning process, longest prefix matching is carried out based on a Trie tree, and a candidate draft sequence is generated by adopting branch backtracking and recursive search; performing confidence evaluation on the candidate draft sequence, calculating a joint confidence score of the sequence through probability multiplication and a Top-K screening mechanism, and judging whether the joint confidence score reaches a confidence threshold; if the accumulated confidence of the candidate sequence reaches a threshold value, skipping a small model generation stage, and directly entering large model verification; otherwise, entering a small model draft completion stage; and the final large model takes the replaced and updated draft sequence as final output. According to the method, adaptive acceleration of the decoding process can be realized, and the long text reasoning delay of the large language model is remarkably reduced while the generation quality is ensured.

There is disclosed a method of automatically detecting a security vulnerability in a source code using a machine learning model, characterized in that the method comprises: obtaining the source code from a client codebase, wherein the client codebase is a complete or an incomplete body of the source code for a given software program or an application; and using a machine learning (ML) model to perform a ML based analysis on an abstract syntax tree (AST) for detecting a first security vulnerability over a static source code, the machine learning based analysis comprise (i) flattening the abstract syntax tree (AST) into a sequence of structured tokens, wherein the sequence of structured tokens comprises a semantic structure and a syntactic structure of the source code, (ii) implementing a natural language processing technique on the sequence of structured tokens for mapping the sequence of structured tokens to one or more integers, (iii) pre-training the machine learning model using an unlabeled source code as an input to predict a subsequent sub-token in the sequence of structured tokens and (iv) training the machine learning model on a labeled source code to predict a presence or an absence of the first security vulnerability.

In some implementations, a device may generate an application creation graphical user interface. The device may receive, via the graphical user interface, one or more user inputs associated with specifying a set of elements of an application; information identifying the application including information identifying the set of elements of the application, each element, of the set of elements, being associated with a corresponding codebase. The device may parse, using at least one security engine of a plurality of security engines, a codebase of an element, of the set of elements, to identify whether the application is associated with a security issue satisfying a threshold severity. The device may generate, based on identifying that the application is associated with the security issue satisfying the threshold severity, a security recommendation relating to the element. The device may transmit an output including information identifying the security recommendation relating to the element.

The invention provides an agent-based code generation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a code generation task and a historical code library, and performing retrieval in the historical code library by a first agent based on the code generation task to obtain an initial code; executing the initial code to obtain an execution result of the initial code, and optimizing the initial code based on the second agent and the execution result to obtain an optimized code; and determining a release standard of code release, verifying the optimized code based on the third agent and the release standard to obtain a verification result, and releasing the optimized code in response to the verification result meeting the release standard. Therefore, according to the scheme, the initial code can be automatically generated, feedback iterative optimization is carried out on the initial code, the development period is remarkably shortened, and the code generation efficiency is improved. And the code is issued after being verified, so that the quality of the generated code can be effectively improved, and the code function is ensured to be accurately realized.

The invention provides a code consistency automatic detection method and system oriented to a large-scale software system. Wherein the heterogeneous grammar relation topology carrying the cross-platform conflict probability is generated through the grammar equivalent mapping relation in the multi-language code feature library, and the heterogeneous grammar relation topology is bound to the abstract grammar tree hierarchy of the target code. And performing multi-core thread coding on the abstract syntax tree by using a parallel computing engine to form a dynamic association path, and adjusting the path based on the conflict probability to generate a mapping relation graph. According to the dependency intensity distribution of the atlas, the code library is divided into edge node subtasks through an edge ad hoc network protocol, computing resources are allocated, and when a high-conflict probability area is detected, a path dynamic reconstruction mechanism is triggered, and a dynamic association path is recombined. And performing cross-node collaborative correction on the dynamic association path based on a distributed consensus protocol to realize efficient detection and optimization of multi-language code consistency. According to the technical scheme provided by the invention, the code consistency maintenance efficiency of a large-scale software system is improved.

The invention discloses an incremental code coverage rate acquisition device based on a Java bytecode dynamic instrumentation technology, and relates to the technical field of software testing and quality control. Comprising a code difference analysis module, a byte code instrumentation module, a coverage rate data acquisition module, a coverage rate data integration module, a coverage rate report generation module and a test trigger and threshold control module, and a syntax tree structure of incremental codes is generated based on a Java syntax analysis tool, so that line level analysis of code differences is realized. According to the method, incremental codes are accurately analyzed through Git and JavaParser, coverage rate data are collected through dynamic instrumentation, a unified report is generated in combination with path analysis, missing scenes are marked in a highlighted mode, and the test efficiency and the code quality control capacity are improved.

The invention discloses a large-scale code library analysis method and device, equipment and a storage medium, and the method comprises the steps: carrying out the first analysis processing of a code file, obtaining a code knowledge graph corresponding to the code file, and storing the code knowledge graph in a graph database; performing second analysis processing on the code file based on the code knowledge graph to obtain semantic logic corresponding to the code file, and storing the semantic logic into a vector database; and performing retrieval based on the graph database and the vector database, and returning a retrieval result corresponding to the query request of the user. The code file is subjected to the first analysis processing to obtain the code knowledge graph, then the code file is subjected to the second analysis processing based on the code knowledge graph to obtain the semantic logic, the code knowledge graph is stored in the graph database, and the semantic logic is stored in the vector database; the interpretability and maintainability of the large-scale code library are improved, and then the accuracy and efficiency of large-scale code library retrieval are improved.

A partitioning technique is applied to divide input code into different portions. Different partitioning techniques can be applied in order to optimize the portioning of the code to account for various features of the code, such as code dependencies. Once partitioned, the code analysis tasks execute in parallel on the code portions. In this way, improved code analysis performance is obtained. Moreover, the addition of new code analysis tasks may not impact overall analysis performance as the partitioning can help to offset added or unknown analysis latency of new code analysis tasks.