1534 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.

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 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.

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 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 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.

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 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.

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 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 DevOps assembly line intelligent generation system based on natural language description. According to the intelligent generation system for the DevOps assembly line based on the natural language description, an interaction layer is responsible for visually editing an interaction interface and outputting a generated executable script and a flow chart to a user; the intelligent processing layer takes Python and Go as development languages, fuses a knowledge graph technology, supports a dynamic code generation capability and realizes intelligent business process management and automatic execution; the infrastructure layer comprises three core modules, namely a metadatabase, a sandbox environment and an execution engine, and is responsible for providing basic support and resource guarantee for system operation. According to the intelligent generation system for the DevOps assembly line based on the natural language description, the configuration threshold of the DevOps assembly line is remarkably reduced, the cross-platform compatibility is ensured, the safety and reliability of the assembly line are greatly improved, and production accidents caused by manual configuration errors can be effectively avoided.

The invention discloses a solution for intelligent low-code construction and automatic deployment of a multi-terminal application full stack, belongs to the technical field of software development, and solves the problems that an existing low-code development platform is high in use threshold, does not support customized development and is disjunction in application construction and application use. The invention provides an intelligent low-code construction platform. The intelligent low-code construction platform comprises a low-code construction platform, an application rendering unit, an application deployment unit and a display interaction unit, the low-code construction platform is used for constructing a plurality of application pages according to the developer personalized configuration information, and associating the plurality of application pages according to set association logic information among the plurality of application pages to obtain an application program; generating application data according to the application program, and storing the application data; the page cross-end rendering unit is used for performing Web end and App end rendering on the application program according to a defined page rendering protocol to obtain a Web end application and an App end application; and the display interaction unit is used for displaying the interaction function and the application logic of the Web end application and the App end application.

The invention discloses a real-time debugging system for software development, and particularly relates to the technical field of real-time debugging for software development, comprising: acquiring target data in a cross-layer call link during real-time debugging for application software development, the cross-layer call link comprising a front-end layer, a service layer and a data layer, before target data enters a cross-layer calling link, structured extraction is carried out, an initial field value and initial attributes are recorded, and the initial attributes comprise permission, currency units, time zones, sources and rule states; by recording and analyzing the processing rule and the change path of the field in the cross-layer call link, reverse derivation and context reduction operations are executed in combination with the structured field mapping structure to recover semantic state information missing from the breakpoint layer, so that the accuracy of debugging judgment and the business consistency of the debugging result are improved.

Systems and methods for streamlining risk modeling in software development using natively sourced kernels are described. The system may receive a native kernel for the first model, wherein the native kernel comprises a native code sample and a native description of the native code sample. The system may input the native code sample into an artificial intelligence model to generate a first output. The system may filter the first output based on the native description to generate a first validation assessment for the first model. The system may generate for display, in the user interface, the first validation assessment.

Disclosed are systems, methods, and non-transitory computer-readable media for a continuous software development and delivery system. Currently, the software development and delivery process is complicated and time consuming. The process requires multiple checkpoints and human intervention to ensure that software updates do not cause errors in the existing software when implemented. Coordination throughout the development process is also problematic as developers may be using varying development environment that are different than the environment used to test and produce the software. The continuous development and delivery system of the present disclosure remedies these issues by providing a consistent development environment throughout the development process. This reduces issues that may arise from use of inconsistent development environments and allows for automation of previously manual portions of the software development and delivery process.

The invention relates to the technical field of software development, and discloses a development platform and a development method of a visual low-code system, and the development platform comprises a data management module which supports a plurality of database types, provides data backup, recovery, encryption, compression and caching mechanisms, and can realize the storage, management and security protection of data; the intelligent service module is used for providing code generation, error detection and repair and authority management services and integrating data mining and machine learning algorithms to realize optimization of a business process; the application development module supports multiple development modes and multi-user cooperative development, and provides intelligent form generation, intelligent code association and intelligent algorithm library functions; the dynamic display module provides a visual graphic operation interface, supports multi-terminal access and real-time data display, and can dynamically adjust service logic and respond to changes of service requirements in real time. According to the method, the development threshold is obviously reduced, and efficient, flexible and internationalized low-code development is realized.

The invention discloses a software development project progress prediction management method based on artificial intelligence, and relates to the technical field of project management, and the method comprises the steps: extracting a causal relation from entity relation graph data, and constructing a causal knowledge graph; a graph structure analysis method and a time sequence prediction method are combined, predicted completion time and delay probability are calculated for the causal knowledge graph and the associated time sequence data, and a progress risk assessment result is generated; performing causal chain identification on the progress risk assessment result and the causal knowledge graph by adopting a causal reasoning method to generate delay interpretation information; according to the delay interpretation information, a preset resource priority and a task weight, a rule driving and priority scheduling strategy is adopted to construct a compensation scheduling scheme; and implementing a compensation scheduling scheme, collecting implementation effect data and analyzing a scheduling effect through a closed-loop feedback and dynamic adjustment mechanism, and generating an optimized scheduling management scheme. The intelligent level and the practical value of project progress management are greatly improved.

The invention relates to a cold and hot data migration method and device, computer equipment, a readable storage medium and a program product, and relates to the technical field of software development. The method comprises the steps of obtaining customer information in response to a trigger operation for a front-end application; inputting the customer information into a pre-trained customer behavior prediction model to obtain a customer behavior prediction result; comparing the customer behavior prediction result with a cold and hot data knowledge graph to obtain associated data corresponding to the customer behavior prediction result and a predicted cold and hot data type of the associated data; generating a migration strategy for the associated data according to the actual cold and hot data type of the associated data and the predicted cold and hot data type; the migration strategy is used for migrating the associated data to a first memory where hot data is located or migrating the associated data to a second memory where cold data is located. By adopting the method, the identification accuracy of cold and hot data can be improved.

Efficient generation of multi-platform compatible source code and test code with consistency and quality in software development SOLUTION: The schema-driven development system includes a user input unit, a schema generation unit, and a code generation unit, wherein the user input unit acquires data in various formats such as a natural language and a CSV file, the schema generation unit converts the acquired data into a schema, and the code generation unit generates a source code and a test code based on the generated schema.SELECTED DRAWING: Figure 1

The invention provides a gray-based continuous deployment method, device and equipment, a medium and a program product, which can be applied to the technical fields of artificial intelligence and software development. The method comprises the following steps: acquiring a first deployment strategy corresponding to a first deployment task, wherein the first deployment strategy comprises the number of nodes participating in gray deployment and the proportion of nodes of different risk levels; selecting nodes from the node pools of different risk levels according to a first deployment strategy to obtain a target batch of nodes; executing the first deployment task in the target batch node; wherein the node pools with different risk levels are obtained by continuously updating according to a second trigger logic, and the updating process of the node pools with different risk levels comprises the following steps: dividing the plurality of nodes into the node pools with different risk levels based on the node features of the plurality of nodes by using a trained risk prediction model, wherein the node features of the plurality of nodes are obtained by continuously updating according to the first trigger logic.

The invention relates to the technical field of integrated circuit design, computer system structures and embedded systems, in particular to a high-real-time interrupt management system and method based on an RISC-V. The system comprises an improved platform-level interrupt controller and an optimized processor internal interrupt processing unit. The system and the method are in tight coupling cooperative work through a special interruption field interface and a system bus, and the system further comprises a core local interrupter, an SRAM, an APB bus matrix and other components. The improved platform-level interrupt controller is responsible for sampling, gating, arbitration, flow control and direct memory access transmission of external interrupt, and a plurality of functional modules are arranged in the improved platform-level interrupt controller; an interrupt processing unit in the processor is responsible for hardware handshake, vector jump, nested control and tail biting mechanism implementation. Through hardware field management, multi-level priority arbitration, hardware nesting and tail biting mechanisms, delay and overhead caused by software intervention in a traditional architecture are eliminated, nanosecond response is achieved, the system throughput rate is increased, the software development threshold is lowered, and the method is suitable for strong real-time scenes.

Apparatus, method, and software of managing dependencies in branches. In an embodiment, a dependency manager is implemented on an automation tool communicatively coupled to a package registry that stores code packages comprising main packages and branch packages. The dependency manager is configured to expect a publish command at the automation tool to publish a branch package for a development branch of main code, identify a name of the development branch, and modify a scope in a metadata file of the branch package with the name of the development branch. In an embodiment, the dependency manager is configured to predict an install command at the automation tool to install one or more dependencies specified for the development branch, and modify each dependency in the metadata file to reference a published branch package corresponding with the dependency when a valid published branch package is found.

The invention provides a software system intelligent development method and system based on an AI technology, and relates to the technical field of software development. Through natural language demand analysis, intelligent component recommendation, code-free generation and continuous optimization closed loop, the problems that a traditional development mode is low in efficiency, insufficient in intelligence and rigid in system are solved, and the method has the advantages that the development threshold is lowered, the development efficiency is improved, continuous optimization of the system is achieved, and data security is guaranteed.

The invention discloses a non-uniform hexahedral mesh generation method based on a region density distribution rule, and relates to the technical field of software development technologies, and the method comprises the following steps: S1, for a solving target, carrying out mesh generation by adopting triangular surface elements, and dividing regions according to different region densities to obtain the maximum and minimum mesh sizes corresponding to each region; s2, after the step S1 is executed, grid lines are sequentially generated in the Cartesian coordinate system according to the x dimension, the y dimension and the z dimension, optimization is started from the area with the minimum maximum grid size, and the maximum grid size is obtained; the method has the advantages that the conversion from a triangular surface element grid to a non-uniform hexahedral grid is realized through modules for identifying and calculating the density distribution of a triangular surface element region, generating three-dimensional grid lines of a Cartesian coordinate system, reconstructing a ray intersecting hexahedral grid, outputting the grid and the like; the problems of mesh generation and geometric target modeling of a finite difference time domain (FDTD) method are solved.

A method for a cyber security appliance incorporating data from a source code repository, hosted by a software development environment, to identify cyber threats related to source code being stored and developed in that source code repository is provided. The method comprises: receiving, at one or more modules of the cyber security appliance, data indicating a network entity representing a user's interaction with the source code repository; and comparing the data, received from the one or more modules, to one or more machine learning models trained on a normal benign behavior interacting with the source code repository using a normal behavior benchmark describing parameters corresponding to a normal interaction behavior. The method further comprises identifying whether the data indicating the network entities interaction with the source code repository corresponds to behavior that deviates from the normal benign behavior; identifying whether a threshold level of deviation from the normal benign behavior has been exceeded; and, if the threshold level of deviation from the normal benign behavior has been exceeded, determining that a cyber threat may be present and executing an autonomous response to restrict the network entities interaction with the source code repository.

Methods and systems for generating live digital documents are provided. The method includes receiving a static document file including human-readable data. Then, parsing the static document file into a plurality of subunits. Then, generating a sharable document splice of the static document file. The sharable document splice includes access to a subset of the plurality of subunits, and the access is provided through an Application Programming Interface (API) or Software Development Kit (SDK) endpoint for each subunit in the subset. Then, executing a digital thread script to generate the live digital document from the sharable document splice and an input digital model representation, using API or SDK endpoints in the sharable document splice. The live digital document is configured through the digital thread script to reflect changes in the input digital model representation. Finally, adding execution metadata for the execution of the digital thread script to the live digital document.

The invention relates to the technical field of software development management, in particular to a cloud platform collaboration method and system oriented to elastic resource scheduling, and the method comprises the following steps: dynamically collecting a development data source and a demand document, determining a developer capability portrait based on the development data source, and determining a task demand portrait based on the demand document; inputting the updated developer capability portrait and the task demand portrait into a preset matching model, and dividing the received to-be-processed project task into a plurality of groups of subtask sets based on the updated preset matching model; and determining a developer matching scheme of each subtask in the subtask set by using the updated optimization algorithm. According to the method, a static skill label is upgraded into a quantifiable and updatable dynamic portrait, an optimization function is constructed by multiple targets such as skill matching degree, load balance, project priority and cooperation cost, and a specific implementation method of an optimal developer matching scheme is solved for an atomic task.