2391 results about "Software development" patented technology

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

Techniques for leveraging a large language model (LLM) in software development are described. A description of an error message associated with a software system hosted by the multi-tenant provider network is received. An LLM is prompted with an analysis prompt to generate an analysis of the error message, the analysis prompt including the description of the error message, and the analysis of the error message is received from the LLM. The LLM is prompted with a suggested resolution prompt to generate a suggested resolution to a cause of the error message, the suggested resolution prompt including the description of the error message and the analysis of the error message, and the suggested resolution is received from the LLM. A change to resolve the cause of the error message is sent to an originator of the received description, the change based at least in part on the suggested resolution.

The invention relates to the technical field of software development management, in particular to an internet software development management system and method. The method comprises the following steps: collecting an original function demand set and carrying out function clustering analysis; extracting and classifying demand function features and generating independent demand units; carrying out dependency analysis on the independent demand units; constructing a dependency relationship set and a demand logic structure; the method comprises the following steps: generating a demand framework matrix, carrying out organization network simulation to obtain a demand connection network, carrying out code reverse evolution according to the demand connection network, carrying out basic parameter matching to obtain evolution basic parameters, carrying out a pressure simulation test on the evolution basic parameters to generate a pressure test result, and capturing resource pressure defects based on the pressure test result. And optimizing and adjusting the evolution basic parameters to generate optimized development parameters. According to the invention, a more efficient Internet software development management method is realized.

Techniques for leveraging a large language model (LLM) in software development are described. A description of a software development task is received from a user. Data associated with the software system is obtained from a data source. An LLM is prompted to identify at least one aspect of the task which requires clarification from the user, at least partly by providing the obtained data to the LLM and asking the LLM to identify a question for the user which remains unanswered by the obtained data. The question is presented to the user. An answer to the question is received from the user. The LLM is prompted to respond to propose an implementation of the task at least partly based on the data associated with the software system and the answer received from the user. The proposed implementation is received from the LLM and caused to be displayed to the user.

A method is provided for optimizing regression testing in a software development environment. The method includes analyzing source code to create a structural dependency graph that maps dependencies between code elements; identifying changes in the source code between a current version and a previous version; mapping the identified changes onto the structural dependency graph to determine affected code elements; selecting a subset of regression tests based on the affected code elements identified in the dependency graph; and executing the selected subset of regression tests to validate the changes in the source code.

The invention relates to a method and a device for generating software based on a large model technology, a medium and electronic equipment, and relates to the technical field of software generation, and the method comprises the following steps: acquiring demand information of a user for software generation; generating a configuration definition file corresponding to the large model generation demand information through a preset code, wherein the configuration definition file is a configuration file of a domain-specific language definition device; performing regular format reformatting processing on the configuration definition file to obtain a processed file; based on the processed file, determining a service flow logic corresponding to a service function in the to-be-generated software, and based on the service flow logic, generating a target software code corresponding to the to-be-generated software through a code generation large model, the format of the target software code being a configuration file format of the domain-specific language definition device; and based on the target software code, through a preset rendering engine, generating a front-end page and a rear-end page of the software needing to be generated. The method has the effect of improving the software development efficiency.

The invention discloses a privacy protection data sharing and cooperative training method and system based on federated learning. The method comprises the steps of receiving software development log data, adaptively judging the sensitivity degree according to a data type, dynamically adjusting noise disturbance intensity according to the sensitivity degree to perform data desensitization, and generating a sensitivity index; selecting a feature extraction strategy, extracting time sequence correlation features from the desensitization data, constructing a dynamic graph structure with a weight, and obtaining a time sequence feature vector through iterative fusion; calculating the time sequence correlation of the time sequence feature vector to obtain a data quality score, and setting a contribution weight based on the quality score to perform parameter aggregation; combining sensitivity indexes with data quality scores to construct a security sharing domain, decoupling global training parameters into knowledge fragments in the domain, formulating a recombination rule, and selectively acquiring the required knowledge fragments by all parties for local training. According to the method, deep collaboration is realized on the premise of protecting data privacy, and the collaboration training effect is improved.

In one embodiment, a method includes detecting a change in context of a computing system to a current context, changing a first registration status of a first set of actions with an assistant system associated with the computing system based on the change in context to the current context, where the first application is in a foreground of a user interface of the computing system, changing a second registration status of a second set of actions with the assistant system based on the change in context to the current context, where the second application is in a background of the user interface of the computing system, updating a list of registered actions for the assistant system based on the changed first and second registration statuses, and storing the updated list of registered actions on the computing system.

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.

The invention discloses a software development task intelligent allocation method and system based on a multi-dimensional capability portrait, and the method comprises the following steps: 1, obtaining multi-source development data generated by a developer in a development process and demand description data of a to-be-allocated software development task, and carrying out the preprocessing, and forming a structured data set; according to the method, a multi-dimensional ability portrait covering technical ability, project experience, collaboration attributes and performance is constructed, a privacy-protected distributed learning mechanism is adopted for dynamic updating, dominant and implicit requirements of tasks are analyzed in combination with natural language processing, complexity and dependency are calculated, and the performance of the performance is improved. A dynamic task feature vector corresponding to a capability feature vector dimension is constructed, and meanwhile, a self-adaptive adjustment mechanism based on real-time data monitoring and online learning is designed to form data closed-loop feedback, so that the problems of one-sided capability evaluation, staticizing task demand analysis and lack of the self-adaptive adjustment mechanism are comprehensively solved; and accurate and intelligent distribution of software development tasks is realized.

The invention discloses a software project development method based on a low-code platform, which belongs to the technical field of software development, and specifically comprises the following steps: receiving original demand description through an embedded natural language parser, and establishing a semantic incidence matrix to extract demand elements; encoding the demand elements into three-dimensional structured demand vectors; mapping the demand vector to a three-dimensional orthogonal feature space, calculating the coordinates of a functional module, and generating an initial topological structure based on a Delou inner triangulation algorithm; rendering an interactive three-dimensional network map on a visual interface; monitoring user operation, and recalculating the adjacency relation of the affected module when the displacement of the module exceeds a threshold value or the connection edge is reconstructed; compared with a historical version, when a key service path is interrupted or a data flow ring is broken, a compensatory connection bridge is activated; a deployable architecture with a version traceability identifier is output, and a traceability code is built in the module to reversely map an original demand vector.

The invention provides a code generation method and system based on a waterfall model and multi-agent collaboration, and the method comprises the steps: constructing a multi-agent collaboration framework which comprises a problem analysis agent, a solution agent, a pseudo-code agent, a coding agent and a restoration agent based on a software development waterfall model thought; and the interaction process of each agent is coordinated through a dynamic cooperation algorithm. Wherein the problem analysis intelligent body checks similar problems and solutions; the solution intelligent agent generates and evaluates candidate schemes; performing scheme conversion by the pseudo-code intelligent agent; the coding agent generates an executable code; and the repair agent performs fine-grained repair on the code in grammar, runtime and semantic levels through a two-dimensional repair mechanism. According to the method, the limitation of a single agent in a complex programming task is broken through, automatic code generation covering the whole process of demand analysis, scheme design, code implementation and test repair is realized, and the quality of generated codes and the reliability of operation are remarkably improved.

The invention provides a digital twinning application-oriented electrical equipment electromagnetic heat flow coupling rapid calculation method, and belongs to the technical field of electrical digital data processing.The method comprises the steps that firstly, a three-dimensional model of electrical equipment is acquired and preprocessed, and a full-order electromagnetic heat flow coupling calculation model is established and verified through a temperature rise test; generating an experimental point matrix by using a Latin hypercube sampling method, and constructing a current temperature power density relational data set; performing regional division on the power density field by applying a K-means clustering algorithm, and constructing an electromagnetic response surface model through a radial basis function; establishing a heat flow field order reduction model based on an intrinsic orthogonal decomposition technology, and extracting a dominant mode primary function; bidirectional coupling of an electromagnetic field and a heat flow field reduced-order model is achieved, an improved Lagrange multiplier method and a fixed point iteration method are adopted for processing the nonlinear coupling problem, finally, a software development kit supporting an open platform communication unified architecture protocol is packaged, and the electromagnetic heat flow coupling rapid calculation capacity needed by digital twinning application is achieved.

The invention relates to the technical field of software development tools, and discloses a micro-application generation system and method based on natural language interaction, and the system comprises four core components: a natural language understanding module, a demand modeling module, a code generation engine and a deployment and integration platform. The natural language understanding module generates structured semantic representation through intention recognition, entity extraction and context analysis; the demand modeling module constructs a data model, a process logic and an interface prototype based on the semantic representation, and optimizes model parameters by utilizing reinforcement learning; the code generation engine generates a cross-platform code of the micro-service architecture through template matching and modularization assembly; and the deployment and integration platform realizes seamless integration of the external system and the Internet of Things equipment through a standardized interface. According to the method, the problems that a traditional low-code platform depends on visual dragging operation, demand analysis is rough and cross-domain adaptation is insufficient can be solved.

The invention discloses a code reconstruction method, system and device based on function recognition and a medium, and the method specifically comprises the steps: scanning a project file topological structure, and constructing a knowledge graph based on a code import relation and a technology stack feature; performing context enhancement by analyzing the code function description and combining with the knowledge graph to generate a code modification instruction set; based on the code modification instruction set, in combination with data stream sensitivity marking and performance portrait analysis, evaluating the target code to obtain an evaluation result; based on an evaluation result, matching a historical optimal practice mode through a graph neural network, and generating an optimization scheme which retains an original design style; by comparing abstract syntax tree differences between original codes and verified optimization schemes, only functional logic nodes are replaced, and legal code style features are reserved. According to the method, the efficiency and quality of code reconstruction and optimization are improved, and a more convenient, efficient and intelligent code processing tool is provided for software developers.

Disclosed herein is a system and method for generating computer code for a plurality of components of a software development project. An artificial intelligence code generator can generate computer code in response to a natural language text input describing a component of the software development project. A first database can store natural language text describing components of the software development project. A second database can store computer code generated at least partially by the artificial intelligence code generator and defining components of the software development project and corresponding to the natural language text stored in the first database. Using a pre-trained language model, the system can generate a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database.

Techniques for displaying a graphical user interface of a software development system are described. An initial description of an initial structure of a software system is obtained, the initial description including an identification of interconnections amongst provider network resources that form the initial structure. A modified description of a modified structure of the software system is obtained, the modified structure based on at least a change to the initial structure of the software system described in the initial description. A difference between the initial description and the modified description is determined. An electronic device is caused to display a graphical user interface including a visual depiction of the initial structure of the software system and an indication of the difference, the visual depiction based at least in part on the initial description.

The invention relates to the technical field of software development, and discloses an automatic UI function test method and system, electronic equipment and a storage medium. The method comprises the steps of receiving a natural language test requirement; intercepting an interface image in real time and transmitting the interface image to a multi-modal analysis module through an API; interface element layout is identified based on computer vision (CV), demand semantics are analyzed in combination with natural language processing (NLP), and a dynamic operation instruction set is generated; driving the automatic tool to execute clicking, sliding and input operations through the API; dynamically adjusting a test path according to execution feedback, and covering a multi-scene case; interface element states, system logs and performance data are monitored in real time, and automatic verification is achieved through multi-dimensional data comparison. According to the method, multi-modal recognition and AI intention understanding technologies are fused, the problems that a traditional UI test script is high in maintenance cost and low in scene coverage rate are solved, non-intrusive self-adaptive full-process testing is achieved, and the testing efficiency is improved by 40% or above.

Disclosed in the present invention is a test case generation method, comprising the steps of: in a graphical tool, editing to form a test model, wherein a scenario, an interaction, and service content are edited, and creating a logic process relationship among the scenario, the interaction, and the service content; in the test model, respectively creating corresponding assertions for the scenario, the interaction, and the service content, and adding the corresponding assertions into the logic process relationship in series; performing test resource scheduling, and configuring a matched use case execution environment for the test model; and compiling the test model into a script which can be automatically executed and executing the script, and after all the assertions are successful, adding the test model into a test case library. Further disclosed in the present invention are a corresponding test management method and system, an apparatus, and a storage medium. By implementing the present invention, the development and automatic test of vehicle-mounted software in an intelligent scenario can be realized, improving the efficiency of a test process and vehicle-mounted software design.

The invention provides an intelligent software development auxiliary system and a use method, and belongs to the technical field of computer software development. The system comprises a project demand analysis module which is used for obtaining and analyzing project demand data and generating feature vectors of demands; the code style optimization module is used for analyzing semantics and styles of the original codes, analyzing and normalizing the original code structures, and generating code structures and style specifications meeting project standards; the intelligent code completion module is used for analyzing context semantics of the codes after style optimization, generating code completion suggestions conforming to project standards in combination with project demand data, and outputting structured completion code snippets; the technology stack intelligent recommendation module automatically recommends an optimal technology stack for the project according to the project demand data and the optimized code structure; and the development behavior risk detection module monitors programming behaviors of developers in real time, captures abnormal operations, marks potential risks and generates operation optimization suggestions.

The invention discloses a software development result traceability analysis system based on version control, and relates to the technical field of intelligent software analysis. According to the method, the non-tampering property of code submission records is ensured through a block chain technology, a credible basis is provided for traceability, the code semantic analysis module generates a data set containing semantic association in combination with a clustering algorithm and weighted calculation, and the function influence positioning module constructs a function association graph by applying a graph neural network algorithm, so that the traceability is improved. An influence path of code change on system functions is automatically identified, the time cost of complex project function influence positioning is remarkably reduced, an evolution path generation module analyzes and tracks a code evolution path in combination with a time sequence, and a core module identification module clusters key nodes and positions a core function module. And the time-tracing source data storage module generates a visual relation graph and a queried data set, so that the whole-process management from code submission to result tracing is realized.

The invention discloses a network data integration analysis system and method based on a model context protocol MCP, and relates to the technical field of computer networks. The system comprises an MCP-LSP adaptation layer, a network analysis language server, an IDE plug-in, a remote cooperation management module, an MCP analysis engine, an LSP client and an IDE UI component. According to the invention, deep fusion of the network protocol analysis capability and the integrated development environment is realized; by constructing an MCP-LSP adaptation layer, a network analysis language server can convert a semantic analysis result of an MCP analysis engine into an LSP standard message format, and standardized services of functions such as protocol analysis, session state and anomaly detection are realized; the working efficiency of a developer is improved by the IDE plug-in; the remote cooperation management module realizes synchronization and sharing of session states, improves integration, expandability and cooperation efficiency of network analysis, and is suitable for diversified requirements of modern software development teams.

The invention discloses an API service management method and device and a medium, and relates to the technical field of software development. The method comprises the following steps: receiving and verifying API service registration information through a standardized interface to generate service metadata based on the API service registration information, and storing the service metadata to a service directory; wherein the service metadata comprises a service name, an interface address and a version number; when the API calling request arrives at the gateway, performing dual verification on the API calling request; wherein the dual verification comprises calling party identity key authentication and verification of types, ranges and necessary items of request parameters; under the condition that the verification is passed, asymmetrically encrypting and negotiating a session key, and performing symmetric encryption transmission on the request and response data by using the session key; and after the transmission is completed, collecting and calling log data to generate a multi-dimensional monitoring chart, and dynamically adjusting the service priority based on the interface error rate of the service provider.

The invention provides a software development data management method and system based on cloud service. The method comprises the following steps: acquiring code version difference data generated in a multi-version development process from a cloud service environment, and allocating the code version difference data to a corresponding storage position according to a version sequence identifier; a version relation graph is constructed based on the calling relation between the version branches, and the data flow direction and the logic dependency relation between versions are dynamically recorded; carrying out sorting and logic dependence verification on conflict contents in combination with the conflict state and the merging strategy, and generating a traceable merging operation instruction set; and finally, executing feedback data according to an instruction set to update a graph conflict state, synchronously triggering storage data update and redistribution of version identifiers, and realizing full-link closed-loop management of development data. According to the technical scheme provided by the invention, the efficiency and accuracy of software development data management can be improved.

The invention provides an implementation method, system and device for a cross-sub-application calling component based on a qankun framework and a medium, and belongs to the technical field of software development. Multiple sub-applications are loaded based on the qankun framework; loading resources of the target sub-application B, and configuring sandbox environment isolation styles and resources; displaying a shared component through the page in combination with the query parameter, an identifier of the shared component corresponding to a value in the query parameter; detecting the currently mounted sub-application B, executing an unloading operation, releasing resources and resetting the state of a mounting container; when the sub-application A is uninstalled by the main application, the target sub-application B associated with the sub-application A is uninstalled, and the consistency of the states of the components called across the applications is ensured. According to the method, the sub-applications and the shared components are found in the modes of universal route matching, parameter setting and the like, and the pages and the shared components are loaded through universal routes and parameters, so that the function of opening the shared components or pages in the sub-applications in a cross-application manner is realized.

Techniques are provided for software deployment pipeline generation using generative artificial intelligence (AI). One method comprises obtaining a trained generative AI model, trained using a plurality of continuous integration / continuous deployment (CI / CD) configuration files; obtaining information characterizing a selected software development project; and applying at least some of the information characterizing the selected software development project as one or more system prompts to the trained generative AI model, wherein the trained generative AI model predicts a portion of a software deployment pipeline associated with the selected software development project. The CI / CD configuration files used for training may be associated with an organization that is associated with the selected software development project. The predicted portion of the software deployment pipeline may comprise pipeline jobs, an automatic code completion and / or a correction of a syntax and / or a structure of at least one CI / CD configuration file being edited.

The invention discloses a software stability evaluation system for software development, which comprises a data acquisition and preprocessing module used for acquiring user behavior data and system performance indexes; the user behavior modeling module is used for modeling the user behavior data based on a psychological model; the abnormal behavior detection module is used for detecting abnormal user behaviors and evaluating potential influences on system stability; the pressure test generation and execution module is used for generating simulation data and executing a pressure test; the causal relationship optimization module is used for optimizing the edge weight of the causal relationship graph through a genetic algorithm; the module ecological modeling and analysis module is used for establishing an inter-module resource competition and cooperation model; the multi-objective optimization module is used for optimizing a resource allocation strategy based on the module health index and the balance demand; and the interpretability and decision-making module is used for analyzing the decision-making process of the evaluation model and generating a decision-making report. According to the method, the stability and fairness of software resource allocation in a complex scene are optimized.

The invention belongs to the technical field of software development and testing, and discloses an artificial intelligence-based software development automatic test case generation system, which is characterized in that an immune heuristic case self-repairing module is adopted, defects are regarded as antigens, antibody cases capable of being self-updated are generated by using a clone selection algorithm, and a gene rearrangement mechanism is automatically triggered when an interface is changed, so that the test efficiency is improved. The details of the use case are adjusted while the core detection logic is reserved; compared with a traditional method, the mechanism can realize use case dynamic adaptation without manual intervention, the maintenance workload is remarkably reduced, and the method is particularly suitable for a complex software system with frequent iteration; the space-time coupling test scene generation engine fuses dynamic scenes such as interaction and state transition of a module and short-time operation after precise coverage login by using a space-time convolutional network; the cross-dimension holographic use case synthesis module integrates multi-source data such as codes, hardware and user behaviors through tensor decomposition to generate a composite use case; functions and performance of software in a complex scene can be comprehensively verified, and test blind areas are remarkably reduced.

The invention relates to the technical field of artificial intelligence and software development, in particular to a context-aware code generation and real-time optimization method and system based on a large language model.The method comprises the following steps that a compiler front-end technology is utilized, input codes are analyzed, and an abstract syntax tree is constructed; function definition, variable declaration and statement block structure information are extracted through an abstract syntax tree, so that syntax and semantic structures of codes are obtained, and basic data support is provided for subsequent code generation and optimization; the method has the beneficial effects that the context graph covering grammar, semantics and runtime behaviors is constructed by fusing static grammar analysis (AST construction) and dynamic execution tracking (such as variable value change and function call time sequence), and the problem of context perception deficiency in a traditional code generation technology is solved. And the compatibility, the maintainability and the expandability of the generated code and the project architecture are improved.