1501results about "Program documentation" patented technology

An example operation may include one or more of receiving a plurality of architecture documents of a plurality of different domains of a software architecture, identifying a missing component that is missing from within the software architecture between a first domain and a second domain among the plurality of domains based on execution of a generative artificial intelligence (GenAI) model based on the plurality of architecture documents of the software architecture, generating a recommended modification to the software architecture based on the identified missing component, and displaying the recommended modification via a user interface.

A system, method, and computer-program product includes identifying a plurality of code synthesis items for a target programming language, generating a code synthesis prompt based on a first sampling of the plurality of code synthesis items, synthesizing, via a large language model, a plurality of raw code segments using the code synthesis prompt, executing the plurality of raw code segments with a code interpreter associated with the target programming language, determining one or more valid code segments of the plurality of raw code segments that the code interpreter successfully executed, aggregating, via a second sampling, the one or more valid code segments into one or more validated code synthesis training samples, and training a code generation model using the one or more validated code synthesis training samples. User interfaces may be provided to allow target coding tasks to be specified via text or speech.

The invention relates to a code generation method and system based on heterogeneous multi-agent collaboration and electronic equipment. According to the method, a special commentator agent instantiated by a heterogeneous large language model is added into a multi-agent collaboration process; according to the method, consistency and feasibility verification is carried out on a demand document given by an analyst agent by adopting a commentator agent, a modification suggestion is proposed in an early stage, deviation and problems are found in time in the early stage of a task, the cost and complexity of later correction are reduced, the robustness and reliability of codes are improved, and the method is suitable for large-scale popularization and application. And the generated result can still maintain high-level correctness under extreme conditions or specific boundary conditions. And the method has practical significance in improving software development efficiency, reducing project risks and improving the quality of an intelligent automatic solution.

The invention relates to the technical field of software development, and particularly provides a self-adaptive code annotation updating method and system, a terminal and a storage medium, and the method comprises the following steps: confirming that codes are updated, extracting an updated code set, and combining and constructing an abstract syntax tree for the codes; performing semantic analysis on the abstract syntax tree by using a deep learning model to generate a semantic vector, and constructing a program dependency graph based on the semantic vector; analyzing a dependency relationship among code elements in the program dependency graph by utilizing a graph neural network; and generating a code annotation based on the semantic vector and the dependency relationship between the code elements by using a natural language generation model. According to the method, the real-time performance and the accuracy of the code document are ensured, the readability and the maintainability of the code are greatly improved, and the maintenance cost is reduced.

The description relates to automated binary code summarization. In one example, a binary code summarization tool receives binary code and combines the received binary code with natural language in a prompt for a large language model (LLM). The binary code summarization tool receives a semantic summarization from the LLM relating to the received binary code and evaluates the new semantic summarization for malicious functionality in the received binary code.

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.

An integrated development environment (IDE) for uses a generative artificial intelligence (AI) model to generate industrial control code in accordance with functional requirements provided to the industrial IDE system as natural language prompts. The system's generative AI model leverages both a code repository storing sample control code and a document repository that stores device or software manuals, program instruction manuals, functional specification documents, or other technical documents. These repositories are synchronized by digitizing selected portions of document text from the document repository into control code for storage in the code repository, as well as contextualizing control code from the code repository into text-based documentation for storage in the document repository.

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.

Systems and methods for artificial intelligence (Al) assisted workflow integration for software development in a digital model platform are provided. In one embodiment, the methods receive a user selection of a target digital tool from a plurality of digital tools that are not directly interoperable with each other. Receive a user request comprising a description of a function script executable by the digital model platform on a target digital model type associated with the target digital tool. Fine-tune a scripting Al agent on prior user actions involving the target digital tool and on a resource-capability mapping of the digital model platform. Generate the function script and generate a unit test script, where the unit test script when interpreted tests the function script against the description. Finally, interpret the unit test script to generate a verification result for the function script; and, generate a test report based on the verification result.

The invention provides a cross-platform low-code application development system based on a Flutter framework. Comprising a login registration module, a project management module, an interface design module, a state management module, a route management module, a team management module, a help document module, an AI page design module, a development document generation module, a hardware integration module, a code generation module, a multi-user collaborative development module, a communication module, an online simulator module, an AI generation application module and an interface management module. An asset management module, a developer forum module, an application market module and an application packaging module. The communication module supports various communication protocols and secure transmission modes, and ensures that data exchange applied to cross-platform and cross-system environments is secure and reliable. Through component dragging and graphical configuration, the low-code platform automatically analyzes configuration data to generate an App, a development document and a source code. And meanwhile, multi-person collaborative development is supported, so that the development threshold is reduced and the development speed is increased.

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 embodiment of the invention discloses a chip register description document generation method and device, a storage medium and terminal equipment, and relates to the field of document processing. The method comprises the following steps: analyzing a register attribute table file of a chip, and determining each functional module included in the chip and a register included in each functional module according to an analysis result; querying an associated register description template document according to the model of the chip; extracting field values corresponding to fields in the register list display area and the register description display area from the register attribute table file, and filling the read field values into a register description template document to generate a register description document of the chip, the problems that in the prior art, compiling of the chip register description document is low in efficiency, prone to errors and poor in timeliness are effectively solved, and a more efficient, accurate and reliable method is provided for compiling and managing the chip document.

A code review is automatically generated by a large language model given a prompt that includes code changes made to a source code program, an associated intent, and an extended context. The intent represents an issue with the code changes from a code reviewer's perspective and is predicted from a neural classifier given the code changes in a code diff format. The neural classifier is a neural encoder transformer model pre-trained on various code review datasets and fine-tuned on code diff hunks of code changes labeled with an intent.

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.

Systems and methods are described for generating code summaries using bifurcated model architectures. The bifurcated model architecture may comprise a first model that generates code summaries based on native code script, and the second model may compare, de-duplicate, and / or categorize the code summaries into clusters that perform the same or similar functions. That is, the systems and methods may comprise a first model (e.g., a large language model) to predict new content (e.g., a code summary for a given native script code). The outputs of the first model are then inputted into a natural language processing (NLP) model to compare the code summaries. For example, the first model of the bifurcated architecture performs a crucial pre-processing step that prevents the NLP model from generating cluster descriptions that are specific to semantic structures, programming languages, and / or code formatting.

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 code generation method, which comprises the following steps that: a code development platform receives input information of a user in a first code file in a project, acquires a context in the file and a first cross-file context according to a static structure of the project, and acquires a second cross-file context according to behavior characteristics of the user in a development project, obtaining a third cross-file context according to the evolution coupling degree of at least one second code file in a code warehouse of the project and the first code file, and generating prompt information according to the input information, the context in the file, the first cross-file context, the second cross-file context and the third cross-file context, and inputting the prompt information into the code generation model for reasoning to obtain at least one group of generation codes, and displaying the at least one group of generation codes to the user. According to the method, the multi-dimensional item-level context and the user input serve as the input of the code generation model, the capability of the code generation model for utilizing the cross-file context in the code generation process is improved, and the code generation effect is improved.

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.

Annotations associated with test cases written to test a software application can allow related test cases to be dynamically distributed among different sets of test cases that can be executed simultaneously in different parallel threads, thereby speeding up testing relative to executing the test cases sequentially in a single thread. The annotations can also allow test cases to be identified that are relevant to code changes, such that testing times can be further reduced by executing a subset of test cases relevant to code changes instead of a full set of test cases.

The invention relates to an automatic code activity graph generation system and method, and the system comprises a plurality of modules. The source code analysis module extracts source codes from the code file and generates an abstract syntax tree corresponding to the source codes. The model conversion engine is based on query view conversion specification operation, analyzes a method calling relation and an execution sequence in the abstract syntax tree by utilizing a group of analysis rules, and converts the source code into a programming language abstract model. And the model warehouse is used for storing the abstract model, identifying the language type through the extension name of the source code file, and matching the corresponding programming language model. The activity graph generator then generates an activity graph according to the abstract model, including an activity graph reflecting code logic and an activity graph generated based on code annotations. The layout module determines the position, the size and the shape of each node in the activity graph through an analysis method calling relation and an execution path, and generates a connecting line between the nodes. The system can visually display the code process and improve the efficiency of developers.

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.

The invention provides a code defect detection method and system based on a large model. The method comprises the following steps: generating an enhanced semantic understanding vector corresponding to an initial source code by utilizing a pre-trained large-scale language model in combination with an attention mechanism; identifying the logic error pattern to determine the defect type and position of the initial source code; the reinforcement learning algorithm selects an optimal repair path by simulating a plurality of repair paths and evaluating the effectiveness of each repair path, and generates a repair code snippet through the optimal repair path; replacing a corresponding part in the initial source code with the repair code snippet, and generating a target source code; performing quality scoring on the target source code based on the code quality evaluation model to obtain a quality feedback result; and outputting the target source code with the change annotation and the quality feedback result. According to the method and the device, the speed and the accuracy of finding and solving coding errors in the software development process are improved, and meanwhile, the quality of repaired codes is also ensured.

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 invention discloses a low-code management system and method based on the AIot Internet of Things, and relates to the technical field of code management, the system comprises a template learning optimization module, a component dynamic adaptation module, a demand conversion module, a code annotation generation module and a code compatibility conversion module; the template learning optimization module is used for automatically extracting optimization points and updating template settings by analyzing historical cases and user data so as to improve the applicability of the template in practical application; according to the invention, through the template learning optimization module, deep analysis of historical cases and user data is realized, optimization points are automatically extracted, template setting is updated, the applicability and development efficiency of the template in practical application are remarkably improved, and through the automatic template updating process, manual intervention is reduced, the development cost is reduced, and the development efficiency is improved. And meanwhile, the stability and compatibility of the system are kept.

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 invention discloses a code version management method based on multi-person collaboration, belongs to the technical field of software development, and in the code version management method based on the GIT distributed version control system, efficient work is guaranteed through modes of code submission, combined submission, code combination and the like. According to an existing cooperation mode, in the development process of one iteration cycle, developers can submit codes for multiple times, and after iteration development is completed through cooperation of multiple persons, many submission records appear, and therefore the code submission records are disordered. Through the technology, after each developer completes the respective task, the submission records are combined into one, so that the readability of submitted information is greatly improved. By means of code submission, merging submission, code merging and the like, high efficiency of work is guaranteed, the code submission management process is standardized, many benefits are brought to software development teams, and the method is an important means for improving software development efficiency and quality.

Apparatuses, systems, and techniques to cause a descriptive representation of graph code to be generated. In at least one embodiment, a descriptive representation of graph code is caused to be generated based on, for example, CUDA or other parallel computing platform code.