616results about "Program documentation" patented technology

The invention provides a legacy system-oriented large-model-driven API document automatic generation system, belongs to the crossing field of artificial intelligence and software development, and provides a multi-modal data fusion and closed-loop verification mechanism aiming at the defects of a traditional API document generation method in the aspects of semantic comprehension, dynamic context capture and multi-technology stack adaptation. A code static feature and a dynamic track during operation are analyzed through a multi-source data acquisition module, and an interface semantic feature is extracted in combination with a field self-adaptive large model of a semantic enhancement analysis module; deducing an implicit service rule by fusing static / dynamic characteristics through a graph neural network, and generating a standardized document conforming to an OpenAPI specification through a parameterized template generative adversarial network (PT-GAN); and finally, performing three-level verification and closed-loop optimization through a sandbox environment. The method supports a heterogeneous system of 16 programming languages such as Java / C + + / Python, interface version changes can be automatically recognized, document patches are generated, the problems of missing and outdated system documents and low maintenance efficiency are solved, and maintainability and integration efficiency of enterprise-level systems are remarkably improved.

The invention relates to the technical field of electric digital data processing, and provides a secondary development code cross-product transplantation method and device, computer equipment and a medium. The method comprises the steps of loading a source project, screening a to-be-transplanted source code file, identifying an original API type, determining a mapping relation between the original API type and a target API type, and generating an adaptation layer code framework according to the mapping relation; copying the to-be-transplanted source code file into the target project; on the premise of keeping the project hierarchical structure of the source project, replacing the calling node of the original API type with the calling node of the adapter type, and replacing the namespace reference with the corresponding namespace in the target project to obtain a migrated source code file; and obtaining a secondary development project of the target project based on the second product API according to the adaptation layer code framework and the migrated source code file. According to the method and the device, the transplantation efficiency and accuracy of the transplantation of the cross-product secondary development codes are improved.

Methods, systems, products, services, and apparatuses for generative AI systems configured to produce computer program code using a knowledge base that includes a client-specific code base, including: receiving one or more input tokens associated with a computer program; accessing information describing a domain-specific codebase; and generating, based on the one or more input tokens associated with the computer program and information describing a domain-specific codebase, suggested code to insert into the computer program.

The embodiment of the invention provides a software development method based on artificial intelligence and related equipment. In the embodiment of the invention, an AI software development method and mode are provided, and a user and a technical design document generation agent cooperate to complete a technical design document of a programming task; and the programming agent autonomously performs task planning based on the technical design document, and autonomously executes the programming task based on the planned execution plan. In at least one of the technical design stage or the task execution stage of the programming task, a man-machine cooperation interface is provided, and the man-machine cooperation interface is used for a user to participate in the software development process for the programming task. Therefore, the software development efficiency and the code quality are greatly improved through division cooperation and man-machine cooperation interaction among the intelligent agents.

The invention discloses a multi-language code generation method based on self-supervised pre-training, which comprises the following steps: acquiring and cleaning multi-language code data to form a training corpus; the method comprises the following steps: representing code data as an abstract syntax tree, extracting a control flow diagram and a data flow diagram of the code data, and obtaining unified semantic representation through combination of a diagram encoder and a sequence encoder; designing a self-supervised pre-training task, and pre-training the semantic representation based on the training corpus; constructing a multi-language pre-training model based on the structure-improved recurrent neural tensor network and the multi-language embedding matrix; when a user inputs a natural language, generating a target language code by using the multi-language pre-training model; and target language code correction is carried out through conventional function testing and grammar checking. According to the method, multi-channel recursive combination and a hierarchical recursive expansion mechanism are combined with self-supervised pre-training, so that accurate generation and performability improvement of cross-language codes are realized.

The invention discloses an automobile industry code quality automatic compliance method based on a large language model. The method comprises the following steps: calling a static code analyzer to generate a violation report; the method comprises the following steps: intelligently classifying violation into Tier1, Tier2 and Tier3 based on a multi-feature analysis algorithm of a rule document; for Tier1 violation, performing three times of retry iteration repair by using a large language model, automatically detecting compilation errors and performing rollback; for Tier2 violation, multiple technical schemes are extracted by a large language model for a user to select; a multi-LLM antagonistic cooperation mechanism is adopted for Tier3 violation, automatic restoration of complex violation is achieved through a Generator generation restoration scheme, a Disscriminator review challenge scheme and antagonistic iteration of Arbiter arbitration conflicts, and an optimal scheme is selected through a weighted voting algorithm when conflicts occur. The method obviously reduces the manual intervention rate, improves the repairing quality, and is low in cost and high in universality.

The invention relates to the technical field of distributed system security and intelligent operation and maintenance, in particular to an API full-life-cycle intelligent treatment method and system based on multi-modal data perception and a blood relationship atlas, and the method comprises the steps: S1, collecting network traffic in a non-intrusive mode, automatically discovering an API, extracting metadata features, and generating an API change event; s2, performing semantic analysis on the API metadata based on a natural language processing technology, calculating a sensitivity score, and performing marking alarm on a high-risk interface; s3, constructing an API blood relationship map, and carrying out change influence analysis and risk assessment based on the map; s4, risk simulation is carried out on API changes based on flow playback, and plan changes are tested in a layered sandbox environment; and S5, automatically generating a document conforming to an open API specification based on the API metadata, and synchronously updating in real time. All links are connected in series to form a closed-loop governance system through the event-driven engine, automatic and intelligent governance of API assets is achieved, and the safety, stability and operation and maintenance efficiency of the system are improved.

The invention discloses a data processing method and device, a medium and a program product in the technical field of computers. According to the application, the code description information and the code positioning question and answer example of each item source code are determined by using the large language model, so that the code description information and the code positioning question and answer example also clarify the related explanation and description information of the corresponding item source code; then, based on the source code of each item, the code description information and the code positioning question and answer example, a retrieval library is constructed, and the retrieval library can comprise comprehensive code related information; finally, code positioning and query are carried out based on a retrieval library, and the situation that a retrieval result deviates from actual code business logic due to retrieval positioning of the code snippets can be avoided to a certain extent; and information such as annotation and architecture description does not need to be manually added for the code, related explanation and description information of the corresponding project source code can be quickly maintained and supplemented, the consistency of the explanation and description information and the code is ensured, and thus the accuracy of code positioning retrieval is improved.

A computer system provides a context for a source code document to an artificial intelligence (AI) model and obtains a first suggested edit at a first source code location. At a code editor user interface (UI), the computer system presents a first chat indicator at the first source code location. Based on user interaction with the first chat indicator, the computer system presents the first suggested edit and receives a user response. The computer system provides an updated context to the AI model. The updated context comprises the context and the user response. The computer system obtains a second suggested edit at a second source code location. At the code editor UI, the computer system presents a second chat indicator at the second source code location. Based on user interaction with the second chat indicator, the computer system presents the second suggested edit.

The invention relates to the technical field of chips, in particular to a chip interface document automatic generation method, electronic equipment and a medium, and the method comprises the following steps: S1, obtaining a to-be-processed interface code file set {F1, F2,..., Fn,..., FN}; s2, analyzing {F1, F2,..., Fn,..., FN}, and generating basic data of the chip interface document; s3, obtaining target document template parameters corresponding to the chip interface document; and S4, generating a target chip interface document based on the document basic data and the target document template parameters. According to the invention, the generation cost of the chip interface document is reduced, and the generation efficiency of the chip interface document is improved.

The invention discloses an automatic code debugging and optimizing system and method based on an AI agent, and relates to the technical field of artificial intelligence and automatic programming, the system comprises a coordinator module, a plan generation module, a code generation module, a test verification module, a deployment operation module, a project summary module, a language model module and a context management module; the coordinator module supports to select and call an Agent for specific task execution according to the current task process, and the plan generation module is configured to automatically generate a development task plan according to the requirements of a user and transmit a task list to the code generation module. Through the exception retry mechanism and the task backtracking mechanism, the system can automatically detect errors in the code generation process and repair the errors, and the tedious process of traditional manual debugging is avoided. The system optimizes the code generation process by backtracking the historical task and adjusting the generation strategy, and the quality of the final code is ensured.

The invention discloses an automatic code complementing and optimizing method and device and a medium. The method comprises the following steps: performing data preprocessing on an obtained code sample data set to generate high-quality training data; training the training data by adopting a pre-constructed algorithm model to generate a code completion model; performing problem diagnosis on the to-be-completed code by adopting a multi-dimensional code diagnosis strategy, and outputting a completion keyword of the to-be-completed code; inputting the completion keyword of the to-be-completed code into the code completion model, and outputting a code completion rule of the to-be-completed code; and complementing and optimizing the to-be-complemented code according to the code complementing rule to obtain a complemented code.

The invention discloses an AI service intelligent fault tolerance and degradation method, device and equipment based on a Java bytecode enhancement technology, a medium and a product, and relates to the technical field of AI service integration. According to the method, in a class loading period of Java application starting, a loaded class is scanned to identify target methods marked with declarative fault-tolerant annotations, a bytecode operation framework is utilized to enhance the target methods so as to implant a unified fault-tolerant logic interception entry, and then in a running period, when the target methods are called, the target methods are subjected to fault-tolerant annotation processing. And triggering a fault-tolerant interceptor through the interception entrance to analyze the annotation to obtain a fault-tolerant strategy, allocating an independent fault-tolerant component instance to the fault-tolerant interceptor based on the unique identifier of the method, constructing and executing an enhanced call chain containing a fusing check and retry mechanism, and finally, if the execution of the call chain fails, executing the fault-tolerant interceptor. And if yes, a preset degradation method in the fault-tolerant strategy is automatically executed, so that complete non-intrusive decoupling of AI service fault-tolerant management and service logic can be realized, and the reliability of the system is remarkably improved.

The invention provides a method for generating a system architecture diagram by automatically optimizing a cue word-driven reasoning large model. The method comprises the following steps of: preparing a multi-mode and reasoning large model and a sample data picture; generating content description and an initial layout requirement text; inputting the combined cue words into a large reasoning model, generating an HTML5 code, and converting the HTML5 code into a target picture; comparing the similarity between the target picture and the sample layout by the multi-modal model, and outputting a difference if the similarity does not reach a threshold value; and optimizing the layout requirement text based on the difference, and iterating until the similarity reaches the standard. Through cross-modal model collaboration, cue word closed-loop optimization, multi-role expert simulation and other technical innovations, the core pain points of strong manual dependence, insufficient flexibility and limited accuracy in traditional system architecture diagram generation are effectively solved, the technical spanning from passive auxiliary drawing to active intelligent generation is realized, and the system architecture diagram generation efficiency is improved. And the method has remarkable advantages in the aspects of automation degree, adaptability and generation quality.

A method for generating technical documentation corresponding to source code is provided. In response to a repository selection received from a user terminal 110, the device 100 receives repository information related to the repository from a code management system 130 (S201 to S203). The device 100 then extracts multiple API code candidates and one or more root file candidates from the repository information (S204). The device 100 then requests a generative AI model (k) to determine paths for the multiple API codes and one or more root files using the extracted file candidates (S205) and receives the determined paths (S206). For each API code, the device 100 requests a generative AI model (l) to generate API documentation corresponding to the API code, and receives and stores the generated technical documentation (S207 to S209).

Disclosed herein are methods, systems, and computer-readable media for generating natural language based on computer code input. In an embodiment, a method may comprise one or more of: accessing a docstring generation model configured to generate docstrings from computer code; receiving one or more computer code samples; generating, using the docstring generation model and based on the received one or more computer code samples, one or more candidate docstrings representing natural language text, each of the one or more candidate docstrings being associated with at least a portion of the one or more computer code samples; identifying at least one of the one or more candidate docstrings that provides an intent of the at least a portion of the one or more computer code samples; and / or outputting, via a user interface, the at least one identified docstring with the at least a portion of the one or more computer code samples.

A text editor or a plugin thereto automatically generates authorship tokens to identify content authored by a human author or an artificial author. The authorship tokens are applied to the work while the work is being produced. Thus, subsequent review of the work can identify regions produced by a human author and other regions produced by an artificial intelligence.

The invention provides a version comparison method and device of a service model and a storage medium. The method comprises the following steps: receiving a comparison request sent by a client to the service model; querying model object instances of the business model under multiple versions according to the comparison request; annotation information is added to metadata in the model object instance; comparing metadata in the plurality of model object instances according to the annotation information to obtain version difference information; and sending the version difference information to the client for display. According to the embodiment, based on a differentiation comparison strategy of annotation information, the object hierarchy and the set element structure with the complex nested relationship in the business model can be deeply analyzed, the change of the attribute value can be detected, deep structure differences such as the change of the object reference relationship and the logic increase and decrease of the set element can be more accurately identified, and the service performance of the business model is improved. And comprehensive and accurate model change information is provided for the user.

The invention relates to the technical field of industrial automation control, and provides a method and system for achieving online simulation of an HMI simulator, and the method comprises the steps: monitoring the starting and offline simulation process of an original HMI programming software simulator, and confirming that the original HMI programming software simulator completes the generation of a configuration file; receiving a mode switching instruction of a user through the mode switching interface; in response to the mode switching instruction, terminating the current simulator process; modifying a configuration file generated by the simulator, modifying an operation mode parameter in the configuration file from a first value representing offline simulation to a second value representing online simulation, and dynamically configuring a communication port parameter; the verification information of the configuration file is updated to ensure the legality of the file; and restarting the simulator, and loading the modified configuration file by the simulator so as to establish communication connection with an external PLC (Programmable Logic Controller) or instrument and enter an online simulation mode. On the premise of not depending on entity hardware, online communication and real-time joint debugging of the HMI simulator and the external equipment can be realized.

Metadata associated with program code documentation is identified, wherein the program code documentation is associated with corresponding program code. A natural language question regarding the corresponding program code is obtained via a virtual agent. A response to the natural language question is determined based on the metadata using one or more trained machine learning models. The response to the natural language question is provided to the virtual agent.

The application provides a code analysis method and related equipment. The method comprises the following steps: a server acquires identity information of a change file and a position of a changed code block in the change file, wherein the changed code block is located in a terminal of a distributed system, and the distributed system comprises a plurality of terminals for providing code writing; the server determines a base version of the change file according to the identity information of the change file; and further, according to the position of the changed code block in the change file, the server determines whether the base version of the change file comprises a target code block overlapping with the position of the changed code block, and if the base version comprises the target code block, the server gives a warning to at least one terminal in the distributed system. The above method can give a warning in time under the condition that the changed code block is determined to be modified. In this way, in the process of collaborative development, a developer can know the modification condition of the code in time, so as to determine whether the modified code will affect the program written by the developer.

In various examples, a technique for resolving a standards violation includes receiving a violation notification of a standards violation detected in a software codebase. The technique also includes determining additional information relevant to the standards violation and included in one or more information sources. The technique further includes generating a prompt based at least on the violation notification and the additional information, generating, using a machine learning model and based at least on the prompt, one or more corrective suggestions associated with the standards violation, and modifying the software codebase based at least on the one or more software code changes.

Techniques are provided for processing rules expressed in a domain-specific language. A computing system may receive a rule including an entity identifier, a property of the entity, and one or more logical operators delimited by syntactic tokens of the language. The rule is parsed into an abstract syntax tree (AST) representation that structurally represents entities, properties, and conditional logic, and the AST is transformed into an executable function configured to evaluate input data objects defined according to a machine-readable data model. Execution of the function on input data produces a logical outcome along with intermediate results corresponding to evaluations of individual conditions and logical operators. The computing system generates an execution trace comprising the logical outcome and the intermediate results, the trace being stored in a structured log retrievable to provide evidence of how outcomes were determined.

The invention provides a software process document generation method and electronic equipment, and relates to the technical field of computers. The method comprises the following steps: based on a document generation demand, retrieving retrieval information corresponding to the document generation demand from a preset retrieval enhancement generation knowledge base through a semantic understanding engine by adopting a retrieval enhancement generation technology; analyzing the target software process document template according to a document generation engine to obtain structure mapping information, and filling retrieval information corresponding to a document generation requirement into the target software process document template according to the structure mapping information to generate an initial software process document; according to the compliance verification engine, compliance verification and rule matching are conducted on the initial software process document, the target software process document passing verification is output, the target software process document passing verification is automatically generated, generation and verification integration is achieved, the generation efficiency of the target software process document can be improved, and the generation efficiency of the target software process document is improved. And the quality of the target software process document is ensured.

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

Compliance of an operating system with safety requirements can be evaluated and monitored using software bill of materials (SBOMs). For example, a system can receive first metadata indicative of a current state of an operating system. The operating system can be used to execute software services, and the operating system can be associated with a functional safety standard issued. The system can further determine whether the current state matches a verified state of the operating system based on the first metadata and based on second metadata indicative of the verified state. The second metadata can be stored in an operating system SBOM. Then, based on determining that the current state matches the verified state, the system can verify compliance of the operating system with the functional safety standard and execute, via the operating system, the software services.

The invention belongs to the crossing field of artificial intelligence and software engineering, and particularly relates to an automatic engineering-level code iteration generation method and system. The method comprises the following steps: acquiring an iteration demand document, an engineering original code and engineering demand document multi-source input, analyzing the engineering original code, constructing a knowledge graph, associating engineering business elements, analyzing the engineering demand document, outputting a structured demand combing result in combination with the knowledge graph, and defining a function boundary and an implementation path. Then integrating an iteration demand document to generate and correct a source code technical document to form an engineering document, performing training optimization, finally generating an initial code by a large model, forming a final code through auditing and debugging, and feeding back data to the model for training, so as to realize full-process automation from demand input to code output. Therefore, manual intervention is greatly reduced, the development efficiency is improved, and the iteration period is shortened.

The invention discloses a cooperative training data generation method for an adjustment and optimization problem and electronic equipment, and the method comprises the following steps: carrying out the filtering processing of an input code description text, and obtaining input data; injecting the problem data into the input data to generate first adversarial sample data containing the problem data; gradient information and disturbance amplitude are calculated, and second adversarial sample data are obtained through adjustment and optimization; designing a shared encoder based on the second adversarial sample data to obtain problem data and normal code data features; carrying out separation processing on the two in a feature space; according to the separated features, the shared encoder is promoted to learn through feature comparison, and finally a model generated by code training data is obtained; and designing an evaluation index for judging whether a generation result of the model in the problem data injection scene is correct and stable, thereby completing model training of code training data generation. According to the method, generalization and robustness of the large language model in the process of processing actual problems are improved.

A system includes a processor that is configured to acquire source code, analyze the acquired source code and extract operational functions and error conditions, automatically generate a user manual and FAQ in natural language based on the generated data, automatically generate images and videos of operation procedures as rich content, and integrate and distribute the generated content via a management system.

The invention relates to the technical field of computer software, and discloses a compilation security monitoring method and system for high-reliability software development, and the method comprises the steps: obtaining an initial source code carrying a security annotation; compiling the initial source code carrying the security annotation to generate an initial abstract syntax tree; analyzing the security annotation in the initial abstract syntax tree, executing deep static analysis, identifying at least one injection point, and determining the type and parameter of the security monitoring logic to be injected for each injection point; selecting a corresponding template from a preset security monitor template library according to the type of the security monitoring logic to generate a security monitoring code segment; inserting the security monitoring code segment into a corresponding injection point in the initial abstract syntax tree to form a modified final abstract syntax tree; converting the final abstract syntax tree back to a final source code file; and compiling the final source code file to generate an executable file. The problem of'black box 'of an automatic tool is solved.