1110results about "Program documentation" patented technology

Provided is a process including: obtaining, with a computer system, access to a code base; decomposing, with the computer system, the code base into parts; generating, with the computer system, documentation for the parts with a language model; associating, with the computer system, the documentation with the parts; indexing, with the computer system, the documentation; obtaining, with the computer system, a query searching for content in the code base; searching, with the computer system, using the index, the code base based on the generated documentation to identify documentation corresponding to the query and, then, content in the code base associated with the identified documentation; and responding, with the computer system, to the query, by identifying the content in the code base associated with the identified documentation.

Provided is a process including: obtaining, with a computer system, access to a code base; decomposing, with the computer system, the code base into parts; classifying, with the computer system, the parts according to content type; selecting, with the computer system, processing templates based on the content types, with at least some different content types having different selected processing templates; and generating natural language documentation for the parts, with one or more generative language models, using the processing templates selected for the parts.

The invention discloses a log quality detection method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring a multi-modal input source; performing reverse anchoring operation in a dynamic analysis stage of the running log to establish direct mapping between the running log and the source code; compiling the abstract syntax tree based on the source code, establishing a grammar dependence graph according to the abstract syntax tree, and traversing the abstract syntax tree to construct a structured data set; based on the running log, obtaining a quality detection result through structured analysis, and generating a semantic detection result by using a large language model; performing cooperative operation on the structured data set and the grammar dependency graph to generate a code log coverage score, and performing arrangement according to a quality detection result and a semantic detection result to obtain an abnormal mode feature; and performing feedback adjustment based on the code log coverage score and the abnormal mode feature in combination with a preset correction rule. The method can efficiently and accurately realize log quality detection, and can be widely applied to the technical field of data processing.

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 data processing, and discloses a standardized document automatic generation method for software development, which comprises the following steps: establishing a document template library which comprises a plurality of document templates formulated according to software engineering standards; the method comprises the following steps: acquiring project data based on a project management system, wherein the project data comprises structured project data and unstructured project data; according to a preset rule engine, automatically filling the project data into the document template to generate a standardized project document; and performing version control and dynamic updating on the standardized project document. According to the method, the document generation efficiency and consistency are improved, redundancy and errors in the manual processing process are avoided, and transparent and standardized management of the whole process of project data is achieved.

An integrated development environment (IDE) leverages a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text. The industrial IDE can also analyze written code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.

A system and method for automatically generating a change history of source code using a generative artificial intelligence (“AI”) system. In examples, a generative AI system receives a request inquiring about one or more changes made to software code of a software service or application. In response to receiving the request, the generative AI system navigates one or more information sources to collect code change context relevant to history of the code change(s). The generative AI system generates an instruction corresponding to the received request, where the instruction and the code change context are provided as input to a language model (LM) (e.g., a generative AI model). Based on the inquiry of the request, the LM processes the input, generates, and provides a corresponding output. The generative AI system then uses the output to generates and provide an explanation about the code change(s) to a requestor of the request.

The invention provides an industrial PLC code generation system and method based on artificial intelligence, and the system comprises an input module, a model optimization module, a code generation module and a deployment module, the input module is used for receiving and analyzing a natural language, a flow chart and an I / O configuration table, and generating a structured logic constraint; the model optimization module is used for supporting low-precision reasoning and dynamic expert network load balancing by adopting an artificial intelligence model of industrial control field data fine tuning; the code generation module is used for outputting PLC codes meeting industrial standards and supporting simulation verification and error correction; and the deployment module is used for downloading the generated code to target PLC equipment or integrating the generated code to engineering software. According to the industrial PLC code generation system and method based on the artificial intelligence, a tool for generating the PLC code based on the natural language of the artificial intelligence is provided, the code development efficiency is improved, and the error rate is reduced.

The invention discloses a dynamic field verification method and system based on user-defined annotations, belongs to the technical field of computer software, and aims to solve the technical problem of how to realize flexible and efficient parameter verification, improve development efficiency and reduce duplicated codes. The technical scheme adopted by the invention comprises the following steps: creating a user-defined annotation: defining a user-defined annotation (at) ValidateFields, and setting up a ValidateFields set in the ValidateFields set in the ValidateFields set in the ValidateFields set in the ValidateFields set in the ValidateFields set; the parameter name list receiving module is used for being marked on an input parameter of a Controller layer and receiving a character string array as a parameter name list; a field verification annotation is created, wherein an annotation (at) ValidateField used for being labeled on an entity class field is defined; creating a verification tool class: creating a verification tool class Validation Util, reading the annotation by using a reflection mechanism, and executing verification logic; and automatic logic verification: using Spring AOP to realize automatic logic verification, intercepting a method of adding (at) Validate Fields annotations in parameters, performing parameter verification through a verification tool class, executing a target method if the verification passes, and returning failure information if the verification fails without manually calling the verification tool class.

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 discloses an automatic function chain generation method and device based on a graph database and a medium, and relates to the technical field of graph databases, and the method comprises the steps: receiving natural language query information triggered by a user, carrying out intention analysis on the natural language query information through a preset language model, determining a structured query condition, and sending the structured query condition to the user; the structured query condition comprises a query intention category, a query entity parameter and logic step disassembly information; according to the structured query condition, mixed retrieval is carried out in a pre-constructed graph database, a candidate API calling path set is determined, the graph database comprises an API knowledge graph and node semantic vector information, and the candidate API calling path set comprises at least one candidate API calling path; and performing multi-dimensional scoring on candidate API call paths in the candidate API call path set, and dynamically generating an executable function chain to determine an API call chain code of the target programming language corresponding to the executable function chain.

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 embodiment of the invention discloses a code conversion method and device based on a domain-specific language and a medium, and relates to the technical field of code conversion, the method comprises the steps that a DSL file and a target conversion language input by a user are received, the DSL file comprises at least one class definition, and the class definition comprises a class name, a field declaration and a method signature declaration; the DSL file is analyzed to extract a plurality of class annotations marked in the class definition, and the class annotations comprise target language identification information, unique class identification information, construction mode identification information and array access mode identification information; and according to the target language identification information in the target conversion language and the class annotations, a matched specified class annotation set is extracted from the class annotations, a code file conforming to the grammar of the target conversion language is generated based on the specified class annotation set, and the specified class annotation set comprises the class annotations, field annotations and method annotations.

A method comprises collecting data corresponding to one or more code changes in response to committing of the one or more code changes to a code repository, and formatting the data into at least one data string. The at least one data string is inputted to one or machine learning models. Using the one or machine learning models, a natural language description of the one or more code changes is generated based at least in part on the at least one data string. The method further comprises causing transmission of the natural language description of the one or more code changes to a document repository.

Systems, methods, and software can be used to generate natural language description of a software code. In some aspects, a method includes: processing a binary code by using a file encoder model to obtain a file embedding vector; and processing the file embedding vector to obtain a text description of the binary code by using a large language model (LLM).

In some implementations, a device may obtain an indication of a proposed change to a subset of executable code from a set of executable code. The device may determine, via one or more models, first context information associated with the proposed change and second context information associated with at least one of the subset of executable code, the set of executable code, or a context of the subset of executable code within the set of executable code. The device may determine a scrutiny level for review of the proposed change based on at least one of the first context information or the second context information. The device may obtain, via the one or more models, review information associated with the proposed change, wherein the one or more models apply the scrutiny level to obtain the review information. The device may perform an action based on the review information.

The invention discloses a project engineering construction method and device, equipment and a storage medium, and relates to the technical field of project engineering construction.The method comprises the steps that in response to a template selection instruction, a target engineering template identifier and an associated baseline version number are determined according to the template selection instruction; generating a target seed engineering compression package according to the target engineering template identifier and the associated baseline version number seed engineering; dynamically loading a latest component version corresponding to the target engineering template according to the version upgrading topological graph and the target seed engineering compression package; and configuring the target environment parameters according to the latest component version, and constructing a target project project. According to the construction method of the project engineering, the process of repeatedly constructing an engineering framework is omitted. And the latest component version is loaded through the topological graph, so that the manual maintenance cost is reduced. The target environment parameters are independently configured according to the latest component version, running errors caused by environment differences are prevented, and the efficiency and reliability of project engineering construction are improved.

Various systems and methods are presented regarding using generative artificial intelligence / machine language (AI / ML) to resolve a merge conflict between two or more versions of a program code. The program code can be industrial automation software utilized to control one or more PLCs in an industrial environment. Interaction with the program code can be via a programming tool that presents the program code in a human readable format, while the AI / ML can be applied to the underlying source code. Hence, while the source code is applied to the PLC, the programming tool enables interaction various human readable summaries of the program code and respective options available to resolve the merge conflict. Accordingly, a process engineer, or suchlike, does not have to be familiar with the source code format to readily understand a summary of the merge conflict or one or more options available to correct the conflict.

Techniques for processing change requests for software projects are disclosed. An example method includes receiving a change request describing new source code for a software project. The method also includes generating, by a processing device, a prompt comprising context information and a plugin, wherein the context information comprises source code from the software project and the plugin comprises text that describes a supplemental work product to be generated. The method also includes providing the prompt to a machine learning model, wherein the context information trains the machine learning model to provide the supplemental work product with a correct content and format. The method also includes receiving the supplemental work product from the machine learning model. The method also includes applying the supplemental work product to the software project.

Automatic annotations and technical specification generation for robotic process automation (RPA) workflows using artificial intelligence (AI) is disclosed. AI / ML models may enable smart searching of workflows and automatically generate documentation for the workflows, including descriptions of each activity, input / output parameters, and overall process explanations. Annotations and documentation may be provided for an entire complex business automation that is the sum of multiple workflows and applications. A Process Definition Document (PDD) for the business process may be generated from the RPA workflow code itself when it does not exist. Other documents, such as audit documents, compliance documents required by laws or regulations, etc. may be produced. The process may be iterative, where a generative AI model automatically converts text to RPA workflow code, a runtime automation is produced from this RPA workflow, and the other documentation is generated as well.

The invention discloses a system, a method and a medium for automatically generating codes for data service of a data platform, and the system comprises a demand analysis module which is used for analyzing a data service demand input by a user, and converting the demand described by a natural language into structured information which can be processed by a computer; the invention relates to the technical field of data processing. According to the system and method for automatically generating the codes for the data service of the data intermediate platform and the medium, user requirements are quickly understood through the requirement analysis module, a data model is accurately matched with a data model through the data model adaptation module, templates are efficiently selected through the code template management module, and codes are automatically generated and optimized in combination with the code generation and optimization module; the manual code writing time is greatly reduced; the interface test module automatically tests the process, a large amount of manual test work is avoided, efficient automation of the whole data service development process is integrally achieved, and the development period is remarkably shortened.

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 relates to a method for automatically synchronizing a code and a development document based on a large model, which ensures that the document and the code are always kept consistent by correlating the document and the code and adopting an AI automatic synchronization mechanism, and avoids various development problems caused by manual updating errors or neglecting updating. According to the technical scheme, the method comprises the steps that a development document link is added into a code annotation, a code address is added into a development document, and bidirectional association is formed; and after the development document is changed, an AI synchronization button is clicked, and the AI automatically analyzes the document change and generates a changed code in combination with the business logic of the code. And after the code is changed, an AI synchronization button is clicked, and the AI automatically analyzes the code change and generates a changed development document in combination with the content of the development document.

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 software engineering management, and discloses a computer software development information management system and method. According to the method, real-time multi-dimensional association among four entities, namely a demand entity, a code entity, a test entity and a deployment entity is realized by constructing a dynamic knowledge graph center, and an end-to-end closed-loop control system is formed; a full-link traceable network is dynamically established through explicit binding, implicit capturing and cross-layer traceability mechanisms, demand omission marks are automatically repaired, and accurate attribution of environmental problems is achieved; manual operation blind areas are eliminated through automatic means such as developing and submitting forced association, automatically freezing branches when test fails and deploying environment consistency verification; based on a risk thermodynamic diagram of full-link topology penetration and fusion defect density and change frequency, active early warning of a high-risk module is realized, and controllability and quality assurance capability of a software development process are remarkably improved.

A plurality of sets input of parameters are captured. The captured plurality of sets of input parameters are input into a first Artificial Intelligence (AI) algorithm that generates a plurality of corresponding AI generated source code. Each set of the captured plurality of sets of input parameters comprises one or more input parameters. The plurality of corresponding AI generated source code are scanned to identify an issue. For example, the issue may be a type of malware or a software vulnerability. A second AI algorithm identifies a first input parameter from the plurality of sets of input parameters that is associated with the identified issue. The second AI algorithm modifies, based on the first input parameter, a new first input parameter provided to the first AI algorithm. The first new input parameter is used to generate a new corresponding AI generated source code.

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.

An information processing device that automatically generates a test code is provided.The information processing device generates a constraint from at least one of a source code, an annotation written in a source code or an annotation of a source code written in an external file, generates a source code that runs on a symbol execution engine and is obtained by symbolizing a motion and an input on the basis of the generated constraint, executes an instruction of the source code generated by the symbolization unit line by line with the symbol execution engine, performs processing in accordance with the instruction reached through the instruction execution to collect path constraints, and solves the collected path constraints using a constraint solver, to generate a test case as a solution of the path constraints.