45 results about "Codebase" patented technology

The application discloses a business code processing method and device, a storage medium and an electronic equipment. It relates to the field of financial technology, and the method comprises the following steps: in the case that a business code repository is detected to have a business code modification event, a target code repository is matched according to a code file name corresponding to the business code modification event; in the case that a code class identical to the code file name is not matched, an attribute tree of a first code class and attribute trees of a plurality of second code classes are respectively constructed; similarity between the first code class and each second code class is calculated according to the attribute tree of the first code class and the attribute trees of the plurality of second code classes, and a target code class is determined from the plurality of second code classes according to the similarity. Through the application, the problem that the processing accuracy of business code is low when the code repository is adaptively modified in the related art is solved.

In some implementations, a system for automatically enforcing review rules within a code repository may receive, from a developer device, a pull request for a code branch. The system may perform a first check to determine whether the code branch has at least one review, in the code repository, associated with the code branch. The system may perform a second check to determine whether the at least one review is associated with a non-contributing user for the code branch. The system may perform a third check to determine whether the non-contributing user is associated with a same organization as the code branch. The system may selectively execute the pull request based on results from the first check, the second check, and the third check

The application discloses a defect code generation method and related device, and the method comprises the steps of: obtaining at least one code segment pair from a code library; analyzing the modification reason between the first code segment before modification and the second code segment after modification in each code segment pair through a first language model, and identifying the defect type of the first code segment in each code segment pair indicating defect repair; obtaining at least one code segment pair corresponding to each defect type as a target code segment pair, and calling a second language model to learn each target code segment pair with the generation of defect code as a learning target to obtain a learned second language model; and generating a defect code segment corresponding to the target code segment through the learned second language model. The method can realize the automatic generation of defect code, improve the generation efficiency of defect code, improve the generation quality of defect code, and enhance the comprehensiveness and authenticity of the generated defect code.

The application discloses a multimodal code search method based on fine-grained attention alignment, constructs a new mechanism to learn the rich semantics of source code and natural language queries from two modalities of text and structure, uses a multimodal feature network to construct a code search model, adopts different construction methods for different modal features, and fully expresses different features of corresponding codes and texts; meanwhile, a fine-grained alignment mechanism is adopted in the model training process, and the same modal features are aligned respectively, thereby facilitating the matching of code entities and text entities; after the construction of all features is completed, a cosine similarity function is used to calculate the similarity between a given query and code samples in all code libraries, so that the code samples are ranked according to the similarity. The application gets rid of the insufficiency of code representation and feature extraction in the existing code search field, can more completely utilize the information contained in different modal features for code representation and matching, and has higher recognition accuracy and better convergence performance.

This application discloses a method, medium, and program product for automatically modifying test scripts, relating to the field of automated testing technology. By automating log analysis, script modification, and script verification for automated testing, it saves the human resource costs associated with these tasks. The method includes: identifying the executed test script and generating an error log; retrieving the test script from the code repository and downloading it to a verification directory; polling historical log information in the log database based on the script name corresponding to the error log to obtain the execution success log corresponding to the script name; retrieving the test script corresponding to the execution success log from the code repository; modifying the test script in the verification directory based on the test script corresponding to the execution success log; and verifying the modified test script in the verification directory; after successful verification, submitting the modified test script in the verification directory to the code repository.

This invention discloses a question-answering method, device, and medium for a computer organization principles course, relating to the field of natural language processing technology. The method includes: First, integrating multi-source data, cleaning and format conversion to construct a question-answering dataset for training. Next, supervising fine-tuning of the ChatGLM3-6B model based on the LoRa method, while simultaneously vectorizing course documents using a semantic vector model to construct a text vector knowledge base containing a vector database and a custom code library, supporting document updates and code retrieval. Finally, a WebUI interface is built based on the LangChain-Chatchat framework, integrating the fine-tuned large model and vector knowledge base, providing users with two question-answering modes. This invention enables efficient fine-tuning of the model based on domain knowledge and constructs a corresponding question-answering assistance mechanism to provide professional, accurate, and tailored intelligent support for teaching needs.

This invention relates to a hybrid Rust, C, and assembly code construction method based on an embedded operating system, belonging to the field of embedded operating systems. This invention utilizes Rust's External Function Interface (FFI) and the "staticlib" and "rlib" code classes supported by the Rust compiler to align the Rust library API with the APIs of the C and assembly libraries in the embedded operating system. This aligns the Rust code with the relocatable files that can be recognized by the embedded operating system, guiding the build system to link the Rust library with the C and assembly libraries to form the final embedded operating system executable file. This invention can link Rust components with C and assembly components to form a single executable file, fully utilizing Rust's type safety and memory safety features to improve the overall security and reliability of the system, while also considering the reuse and expansion of existing code libraries.

Methods and systems are described for detecting and reporting a vulnerability fix in a code repository. A commit obtained from the code repository is preprocessed to generate file-level token sequences each representing a file-level code change for respective files. Respective file-level code change embedding vectors are generated by inputting each file-level token sequence into a transformer model, each file-level code change embedding vector being a vector representation of the file-level code change for the respective file. The file-level code change embedding vectors are combined into a commit-level code change embedding vector that represents all code changes contained in the commit. A predicted commit-level vulnerability fix score is generated by inputting the commit-level code change embedding vector into a classifier. A vulnerability fix report is outputted, containing an identification of the commit and the predicted commit-level vulnerability fix score.

The techniques disclosed herein provide a system for automated deprecation of software codebases using a self-training analysis agent and a weighted input representing a plurality of codebase factors. Generally speaking, operating a cloud computing platform is complex and can involve managing a large number of software codebases including source code, infrastructure, configuration, and user applications. Consequently, as new products and / or services are released, technology advances, and various software codebases age, portions of the cloud computing platform may become vulnerable to errors, malicious actors, and other undesirable behaviors that lead to degraded service quality. The system presented herein utilizes a self-training analysis agent to automatically identify a given software codebase for deprecation using a weighted input representing a plurality of factors relating to the codebase such as code type, a code age, and usage metrics. Accordingly, the codebase is archived for a predefined time period prior to final deprecation.

Methods, systems, and computer-readable storage media for receiving a current story representative of a function that is to be added to an application, generating a current story embedding, determining a set of historical stories at least partially by comparing the current story embedding to historical story embeddings in a set of historical error embeddings, identifying a sub-set of historical stories from the set of historical stories, the sub-set of historical stories including one or more historical stories, retrieving a historical code snippet and a test case set associated with each historical story in the sub-set of historical stories, generating a code snippet for the current story using a large language model (LLM) system, and releasing the code snippet to a code repository for integration in the application.

A method and system for generating an Application Programming Interface (API) path utilizing code detection is presented. The method includes detecting a resource, wherein the resource is deployed in a cloud computing environment; detecting at least a code object of a plurality of code objects stored in a code repository, from which the resource is deployed; inspecting only the at least a code object from which the resource is deployed for a hint of an API path; generating a potential API path based on the hint of the API path; executing a network access instruction in the cloud computing environment based on the generated API path; and determining that the potential API path is a validated API path based on a response of executing the network access instruction.

The present disclosure provides a method (300) and a system (130) for error detection and correction in continuous integration / continuous delivery (CI / CD) pipelines. The system (130) executes a CI / CD pipeline based on updates to a software received from one or more development devices (110) and detects errors in the updates and / or in the code stored in a repository (220) by processing log data generated during the execution of the CI / CD pipeline using a local AI model (140).The system (130) then executes a first error correction script generated by the local AI model (140) to correct the detected errors, and iteratively executes a second error correction script generated by an external AI model (150) for errors that persist after the implementation of the first error correction script, until a stop condition is met.

Privacy incidence evaluation systems and methods are disclosed herein. A code changeset is retrieved that identifies a change from a prior version of a code. A tokenization score is determined from keywords present in the code changeset, where the tokenization score is indicative of a presence of a privacy term in the code changeset. A historical score is determined based on a set of computer code stored in a code repository. A privacy risk score is generated from the tokenization score and the historical score, where the privacy risk score indicates a likelihood that the code changeset, if implemented, would result in an occurrence of a privacy incident (e.g., an unintended leak of private data). An action is performed based on the privacy risk score to mitigate a risk of a privacy incident occurrence.

The application relates to a demand document automatic processing method and system, computer equipment and a storage medium, the method comprising the following steps: performing knowledge analysis on a project code library related to a demand document to extract project information and project specifications, and compressing the project information into a structured project knowledge base; automatically generating a demand analysis report based on the demand document and the project knowledge base; generating a demand scheme document conforming to the project specifications based on the demand analysis report; and generating complete code conforming to the project specifications and capable of being compiled and run based on the demand analysis report. The application can automatically generate a demand scheme and code of a demand document based on an AI agent, improves the generation efficiency of the demand scheme and code of the demand document, and ensures the correctness and functional integrity of the generated demand scheme and code.

The application relates to a large model-based code search method, which comprises the following steps: obtaining a target code library to be searched; performing reasoning analysis on code segments in the code library through a code understanding intelligent agent, taking extracted semantic information as IOF data; converting the IOF data into a feature vector, storing the feature vector into an initialized vector database, and constructing a mapping index of an IOF field to an original code segment; analyzing the semantics of a user query through a query understanding intelligent agent and generating initial search metadata; performing preliminary search in the vector database based on the search metadata to obtain a candidate list; constructing an iterative search loop containing an evaluation intelligent agent, the evaluation intelligent agent judging the matching degree of the current candidate list and the query, if a termination condition is not reached, re-generating search metadata and performing incremental search; when the termination condition is met, performing semantic reordering on the accumulated candidate IOF data through a reordering intelligent agent, and finally outputting recommended code segment results.

The present application relates to code catalog management technical field, especially for a kind of standardization code catalog management method and system, the present application constructs the standard catalog code base covering multiple geological elements, different geological elements are cataloged data using the mode of subtab classification collection, geological feature change node is automatically identified based on the depth interval of code in each subtable, complete continuous mutually exclusive geological layering processing, through preset description template and code semantic mapping rule, standardization description text in line with geological specification is automatically generated, finally geological layering result, description text and original catalog data are associated storage, realize achievement bidirectional tracing and standardization output.The present application solves the pain points of traditional geological catalog standard not unified, low efficiency, achievement not standard, data not traceable, realizes geological catalog whole-process standardization and automation, greatly improves cataloging operation efficiency and achievement quality.

Provided in the embodiments of the present disclosure are a data construction method, a code question-answering method, a task platform and a code question-answering system. The data construction method comprises: acquiring a first code snippet and an initial sample question for the first code snippet; using a text processing model to rewrite the initial sample question by using directory structure information of a sample code repository in a code library as context information, so as to obtain a sample question; on the basis of the first code snippet and the sample question, recalling a related second code snippet from the code library; and using the text processing model to generate, by using the second code snippet as context information, a sample answer corresponding to the sample question, wherein the sample question and the sample answer are sample data used for training a code question-answering model, and the code question-answering model is applicable to the code repository. The data construction efficiency is improved, and the data construction cost is reduced; and a model obtained by means of training is adapted to a code repository, thereby improving the user experience.

The present disclosure provides a method for learning a project code base and locating error codes, comprising: constructing a knowledge graph of a project code base based on a code framework of the project code base; generating an exploration task based on the knowledge graph; formulating an action plan comprising specific exploration steps and retrieving associated code snippets based on the exploration task; and learning the retrieved code snippets by executing the action plan to update the knowledge graph. The present disclosure can automatically learn the knowledge of the project code base.

This application discloses a method, apparatus, device, and storage medium for adapting the UI layout of an in-vehicle device, relating to the field of vehicle engineering and applied to a target in-vehicle device. The method includes: launching a target application to be adapted on the target in-vehicle device; using the target application to read a target configuration file locally on the target in-vehicle device and parsing the target configuration file to obtain the corresponding parsing results; the target configuration file is a JSON configuration file; adjusting the UI components of the target application based on the parsing results, and adapting the business logic corresponding to the adjusted layout based on the logical rules in the target configuration file to obtain the corresponding target layout; initiating a display request for the adapted layout using the target application; restarting the target application based on the request result to display the main activity interface of the target application based on the target layout. This application achieves efficient and flexible dynamic adaptation of UI and business logic across multiple platforms using a single codebase.

An artificial intelligence searching tool for enterprise specific code generation includes receiving a first version of a new functionality entry that includes multiple new pairs including a new prompt and a new response referencing a corresponding new code example and storing the new functionality entry in a clone of a code repository. The method further includes processing, by an LLM using information from the clone, a set of test prompts selected from the new pairs and the existing pairs. The method further includes identifying an accuracy level of the set of generated responses in selecting, from the clone, a corresponding code example for each of the set of test prompts, detecting, based on the accuracy level, a conflict between the first version and an existing functionality entry, and populating an owner interface with information regarding the conflict to obtain a second version of the new functionality entry.

A system includes a memory configured to store a software codebase and an instance of vulnerabilities associated with the software codebase. The system further includes a processor coupled to the memory and configured to access the software codebase and execute a scan to identify sets of metadata associated with the software codebase. The processor is configured to execute a first machine-learning model trained to generate a prediction of feature clusters based on the sets of metadata. The plurality of feature clusters includes a ratio of a vulnerability to a time-intensiveness associated with remediating the vulnerability. The processor is configured to execute a second machine-learning model trained to generate a prediction of associations between the plurality of feature clusters and the instance of vulnerabilities. The prediction of associations includes an estimate of a time-intensiveness for remediating the instance of vulnerabilities.

This invention discloses a method and apparatus for porting the open-source HarmonyOS system to a Linux kernel. The method includes: obtaining adaptation layer code built based on kernel features of the open-source HarmonyOS framework; performing HarmonyOS-ization on the Linux kernel of the target board to obtain a kernel image file; compiling the adaptation layer code and the target code of the open-source HarmonyOS system based on kernel compilation parameters and target compilation tools to obtain a target image file; and starting the open-source HarmonyOS system on the target board based on the kernel image file and the target image file. The technical solution of this invention can support Linux, Android, and OpenHarmony systems simultaneously with a single kernel codebase, significantly reducing labor costs and shortening the development cycle.

The present application relates to a kind of machine learning code library memory fault injection vulnerability detection methods based on bit flip, comprising the following steps: S1, add extension function to programmable compiler, insert detection instruction to sensitive code in machine learning code library, to detect the code point position at risk with dynamic analysis method;S2, using machine learning model and data set under actual application scenario, collect running data, calculate and find risk sensitive code point position, i.e. the imbalance degree of sensitive instruction is calculated and analyzed, determine the risk sensitive instruction set vulnerable to memory fault injection vulnerability;S3, by injecting bit flip into actual memory hardware, obtain the fault injection vulnerability distribution profile of actual memory hardware;S4, match piece by piece, obtain vulnerability sensitive instruction set.The beneficial effect is to reduce or remove the risk and harm caused by tampering machine learning code library, ensure the security and integrity of machine learning model.

Methods and systems for providing computer-implemented services using a data processing system are disclosed. To provide the computer-implemented services, a dynamically generated set of changes for a codebase including source code for a software program may be iteratively evaluated until a final set of changes meeting change criteria is obtained. The change criteria may be adapted to discriminate whether the final set of changes is likely to introduce undesired operation of an updated version of the software program. Using the final set of changes, an integration process may be performed to obtain the updated version of the software program to continue provision of the computer-implemented services.

The application discloses a one-stop intelligent software development system based on multiple agents, which comprises a one-stop DevOps research and development cooperation platform and a multiple-agent scheduling and cooperation engine. The multiple-agent scheduling and cooperation engine is connected with each sub-module in the one-stop DevOps research and development cooperation platform, including a demand management module, a code library management module, a document management module, a pipeline module, a product library module, a code scanning module, a test management module and a measurement management module. The multiple-agent scheduling and cooperation engine comprises a task analysis and distributor, a multi-role virtual employee intelligent agent pool, a large model calling adapter, a delivery material archiving and integration controller and an artificial confirmation node manager, realizes end-to-end automation from demand description to software delivery, and only needs to fill in a natural language demand description or a prompt word in a task card, so that the system can automatically drive a whole-process task such as prototype generation, code writing, test case generation and construction deployment, and the software delivery cycle is greatly shortened.

This invention provides a method, system, device, and storage medium for constructing a multi-agent microservice model. The method includes: receiving a target microservice codebase; launching a multi-agent collaborative system based on a large language model; this system extracts field source mapping relationships and business processing logic by calling a code parsing tool and combining it with thought chain reasoning, generating structured lineage data and system model data; sending the generated data to a message middleware; asynchronously consuming the data; persisting the text containing field processing logic to a relational database; and persisting the edge relationships between service nodes and field flows to a graph database. This invention overcomes the context overload and illusion problems when a single model processes a large codebase by splitting multi-agent tasks and synchronizing lightweight local library states, giving the system breakpoint resume capability and achieving transparency and real-time synchronization of technical assets and business logic.

The application provides a method and device for importing a standard module library in software engineering, the method comprising designing a standard module library by adding an api directory and an info.json file in the root directory of the module library, realizing the external interface and configuration of the standardized code library; and importing the standard module library into the main project by using an import tool. The application provides a design standard for an Android engineering function module library and an engineering import tool for the standard, simplifies the import of module functions in Android engineering development, standardizes the import of module functions, and reduces the learning cost of the library. By realizing the standard design of the module library, using a visual import tool, and dynamically adding the corresponding calling code file in the project, the application realizes the efficient import of the Android engineering module library and reduces the learning cost of using related functions.

Disclosed is method for automated code retrieval and code generation, comprising: receiving code repository (302); extracting coding components (CC); generating knowledge graph (KG) comprising nodes representing extracted CC, and edges representing relationships between the extracted CC; identifying node(s) in generated KG for indexing; creating first index (FI) (406, 512) and second index (SI) (408, 514) of generated KG; receiving user request (502) for generating code; searching FI and SI based on entity(s) (510) extracted from user query (UQ) and embeddings (506) of UQ, respectively, for identifying predefined number of nodes (PNN) (516, 520A) in KG; retrieving identified PNN from KG; retrieving neighbouring nodes (NN) (520B-C); generating subgraph of retrieved NN and relationships between retrieved NN; filtering generated subgraph (GS); retrieving code snippets (404C) of retrieved NN; generating contextualized subgraph; and prompting Large Language Model (608) with generated contextualized subgraph, predefined KG ontology and area of interest(s), for generating code (610).

Central package management (CPM) across code repositories is disclosed. In an example, a primary “props” file in a Git submodule references a version of a NuGet package. A “.props” file in a root directory of each of multiple code repositories references the primary props file in the Git submodule. Each of multiple software projects in the code repositories has a package reference to the NuGet package without a reference to the version of the NuGet package. When each of the multiple the software projects is built (e.g., compiled), the build process automatically uses the version of the NuGet package that is identified in the Git submodule. This permits a single change to the version number of the NuGet package in the Git submodule to be propagated across multiple projects spanning multiple code repositories.

In some embodiments, an RPA design interface exposes a set of DevOps activities for creating a DevOps pipeline. Exemplary DevOps activities include activities for pulling a target RPA workflow from a code repository, testing, building, and deploying the respective RPA workflow to a client's production environment. The pipeline may then be executed by conventional RPA robots, as any other RPA workflow. Endowing an RPA design tool with DevOps functionality substantially facilitates RPA development by enabling RPA developers to carry out DevOps without requiring specialized knowledge of DevOps software. Some embodiments further enable RPA developers to create complex DevOps pipelines combining DevOps activities with other robotic activities, such as interacting with GUIs, spreadsheets, and electronic communication applications, among others.