812results about "Software maintainance/management" patented technology

The invention discloses an AI low-code development component automatic generation method based on a knowledge graph, and relates to the technical field of AI low-code development, and the method comprises the following specific steps: constructing an AI development knowledge graph: constructing a knowledge graph containing a five-layer structure, collecting and preprocessing multi-source data, and extracting various entities and association relationships, so as to obtain an AI development knowledge graph; constructing an ontology structure, importing the ontology structure into a database, and forming a final knowledge graph through manual auditing and machine reasoning optimization; according to the method, the multi-dimensional AI development knowledge graph is constructed, user requirements are analyzed in combination with the optimized natural language processing model, the component configuration scheme is generated by introducing the multi-level reasoning mechanism, and finally the standardized low-code component is automatically generated, so that full-automatic conversion from the user requirements to the low-code component is realized, and the user experience is improved. The defect that an existing low-code platform depends on manual component configuration is overcome, the development period is greatly shortened, and the development cost and the technical threshold are reduced.

Disclosed herein are methods and systems to generate code for a workflow. A non-limiting example of a method comprises executing, by a processor, a large language model, which receives an input of an intent associated with a workflow and provides a machine-readable description by: identifying, by searching a hierarchical data structure using a vector embedding associated with the intent, an object corresponding to the intent; identifying a set of data paths within the hierarchical data structure to retrieve the identified object; and generating the machine-readable description, the machine-readable description describing the object and at least one data path within the set of data paths; and generating, by the processor, code for the workflow using the machine-readable description.

The invention provides a multi-rule static detection and large-model dynamic repair method for code defects. The method comprises the steps that code defect types are summarized and abstracted into a unified defect mode rule set; constructing an abstract syntax tree, a control flow diagram and a data flow diagram of the source code; performing static analysis based on a multi-rule engine to identify potential defects; inputting the defect context into a large language model to generate a repair suggestion; performing semantic consistency verification and integration on the repair suggestions; and executing automatic testing and secondary static analysis to verify a repair result. According to the method, the traditional static analysis technology and the modern large language model capability are combined, accurate recognition and intelligent repair of code defects are achieved, a closed-loop defect processing flow is formed, the defect detection accuracy is improved, intelligent repair is achieved, closed-loop verification is formed, the development efficiency is improved, and man-machine cooperation is supported.

To make a meta model as a use destination meta model to be easily used by various systems with the meta model designed by certain system development as a reference source meta model.SOLUTION: A design support system updates a use destination data structure while making a reference source data structure unupdated when a use destination meta model uses a reference source meta model. Since the reference source data structure is made unupdated, the reference source meta model is maintained without changing a content even though the reference source meta model is used. Meanwhile, since the use destination data structure is updated, the use destination meta model can be created again with an updated content. For example, when the reference source meta model is upgraded, the updated use destination data structure can be applied to an upgraded reference source meta model.SELECTED DRAWING: Figure 18

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 provides an end-to-end intelligent system development method and system based on an AI large model. The method comprises the following steps: S1, receiving a system demand described by a user through a natural language; s2, analyzing the requirements through an AI large model and converting the requirements into formalized specifications; s3, automatically generating a system architecture and dividing components; s4, generating each component code in a recursive manner; s5, dynamically detecting and repairing the generated code problem; s6, automatically integrating all components to form a complete system; and S7, continuously optimizing the system based on system feedback or user feedback. According to the method, work of multiple stages such as demand analysis, architecture design, module division, code writing and testing is automatically completed, distributed integration and smart contract are performed, distributed integration verification is performed through a block chain smart contract and a graph database, data consistency, interface matching and security among components are ensured, the integration process is optimized, and the integration efficiency is improved. Cross-domain cooperation is achieved, and the development efficiency is high.

The invention provides a code peculiar smell detection method and system based on a large language model, and relates to the technical field of code peculiar smell detection.The method comprises the steps that context code information of a to-be-detected code is extracted, and the context code information comprises a method code and a class code; respectively extracting key information of the method codes and the class codes, and constructing a method information set and a class information set; constructing a large language model; on the basis of a thinking chain and a thinking tree, constructing cue words of the large language model; constructing a hierarchy-based code replacement strategy; dynamically adjusting the context code information by using the method information set and the class information set according to the code replacement strategy; and inputting the cue word and the dynamically adjusted context code information into the large language model for code odor detection, and outputting a code odor detection result of the to-be-detected code.

The invention discloses a design manuscript code generation and visual error iterative correction method and system based on multi-agent cooperation, and aims to solve the problems of low front-end code generation efficiency, poor visual restoration degree and lack of an automatic correction mechanism in the prior art. The system constructs a four-node state diagram based on LangGraph, automatically initializes a series engineering scaffold, obtains design data and generates a structured protocol, and generates a component code based on the protocol and performs visual verification and iterative correction on four processing nodes. The method comprises the following steps: dynamically generating an engineering scaffold through a large language model agent, converting Figma design data into a structured protocol, and carrying out iterative optimization by utilizing a judger-repairer mode until an error is lower than a threshold value; according to the method, high-fidelity automatic delivery from the design manuscript to the code is realized, and the executable front-end engineering code and the visual reduction degree analysis report are output.

An architecture includes multiple agents that communicate through structured dialectical exchanges to transform business requirements into executable applications. The agents are configured to collaborate and communicate through a series of iterative dialogues, where each agent contributes its specialized expertise to the process. The structured exchanges allow the agents to challenge, refine, and validate each other's outputs to automatically generate an application.

This invention discloses a full lifecycle management system and method for scientific computing programs. The system includes a build environment subsystem and a production environment subsystem. The former provides computer resources for the build process of the scientific computing program throughout its lifecycle, while the latter provides computer resources for the testing and deployment processes. This invention, through a full lifecycle management method for scientific computing programs, operates on corresponding computing resources, encompassing a series of steps including querying, triggering scheduling, build execution, result distribution, and test deployment. It automatically generates the executable file of the scientific computing program and configures its runtime dependencies. Simultaneously, it automatically generates a corresponding description file recording the entire lifecycle process. Furthermore, based on version management of these description files and their sets, it achieves full lifecycle traceability and cross-platform migration and deployment of scientific computing programs in a high-performance computing environment.

Code and text processing and analysis tools are provided for finding potentially sensitive information in a corpus of code. A request is received to analyze a corpus of code or text to find potentially sensitive information. Information unlikely to be sensitive is removed from the corpus of code or text to generate a modified copy of the corpus of code or text. Frequently occurring sections may also be removed as unlikely to be sensitive. An unpredictability score is determined for the remaining sections of the modified copy of the corpus of code or text, and the unpredictability score is used to prioritize unpredictable sections for review. Selectable section identifiers of the prioritized sections may be sorted in a code or text navigation interface and reviewed in a code or text viewing interface to determine whether the selected sections incorrectly contain sensitive information. If so, corrective actions may be triggered.

The invention relates to the technical field of plug-in resource management, and discloses an adaptive plug-in scheduling and dynamic optimization evolution method and system, and the method comprises the steps: obtaining a plug-in task generated by an edge calculation module based on a target device real-time monitoring index data flow; monitoring running state data of the plug-in system in real time and preprocessing the running state data; carrying out resource allocation on the plug-in task by utilizing a reinforcement learning algorithm based on the preprocessed data, and generating a plug-in task resource allocation result; performing plug-in resource allocation on the plug-in task and the plug-in task resource allocation result based on a virtualized resource pool mode to generate a plug-in resource allocation result; and generating a plug-in code based on a plug-in resource allocation result by utilizing a model-driven development and automatic code generation technology, and running a feedback result to optimize and evolve a plug-in. Through an intelligent and adaptive mechanism, the real-time response capability, the performance, the expandability and the sustainable development capability of the plug-in are remarkably improved.

The invention discloses a C + + Mock class automatic generation method based on an inverse memory agent and a contrast knowledge graph, and aims to solve the problems that a virtual function coverage gap in a large C / C + + code library is difficult to find, the Mock code maintenance cost is high, and the automatic repair efficiency is low. According to the method, a difference context matrix is constructed by analyzing and merging request patch metadata, and an incremental abstract syntax tree is generated; generating a backtracking queue based on the event intensity and the semantic overlapping degree, and extracting a virtual function change list; the multi-scale difference extraction is completed in combination with an external expansion operator according to the rule that the path is not disassembled and the node is movable; a self-adaptive strategy table and a Mock specification are generated, through code synthesis and lightweight compilation verification, automatic self-healing is achieved through root cause positioning and minimum cost repair, finally, a result is written into a knowledge graph, and parameters are dynamically adjusted. According to the invention, Mock generation accuracy and version adaptability are improved, manual intervention is reduced, and test maintenance efficiency is improved.

There is provided a system and method of automatic software testing. The method includes obtaining an input including software code of a software program and metadata, and feeding the input to a machine learning model to generate a test suite usable for testing the program. The test suite comprises a set of tests meeting a predefined condition. The test suite is generated by generating at least one question related to at least one of: expected intents of one or more sections of the software code, or tests for testing the sections, and presenting the at least one question to a user; upon receiving feedback from the user, analyzing the feedback with respect to the predefined condition, and determining whether to generate at least one new question; and, in response to an affirmative determination, repeating the above process with respect to the new question, until the predefined condition is met.

The invention relates to the technical field of model cross-scene application, and discloses a data model cross-scene management method and system.A judgment matrix is constructed through an analytic hierarchy process to quantify cross-scene demand importance, cross-scene demand priorities are sorted, a cross-scene flow chart is drawn, and a standardized data management system is constructed. A cross-scene data model is designed by adopting a layered architecture based on a standardized data management system, system docking and monitoring are realized through an AP I interface, service attribute characteristics and interface acquisition information of a scene are extracted, a scene data scale type and a real-time type are divided through a clustering algorithm, and a scene data real-time model is obtained; and distributing a basic computing power strategy for the scene according to the established computing power distribution strategy database, judging the data feature stability of the cross-scene data model in each scene by calculating a comprehensive drift index, and if the stability is low, triggering computing power distribution adjustment.

The invention provides a software bill of material analysis method, which comprises the following steps: S1, selecting a software project to be evaluated, and ensuring that a code library of the software project is complete and accessible; s2, scanning the code library and the dependency relationship by adopting an analysis tool, and generating a software component report; s3, identifying a three-party service used in the software project according to the information obtained by scanning the code library and the dependency relationship; s4, according to all the content of the code library obtained through scanning and in combination with the recognized three-party service, self-research codes are accurately distinguished, and the code self-research rate is calculated; and S5, identifying key elements, structures, defects or security vulnerabilities of the codes, comparing the key elements, the structures, the defects or the security vulnerabilities with known correct results or expected results for analysis, and judging the quality of the self-research codes. According to the method, bill of material analysis can be comprehensively and accurately carried out on software projects, self-research codes are clearly defined, three-party services are effectively identified, and code quality is accurately evaluated.

The invention discloses a software development evaluation method and device based on multiple dimensions and electronic equipment, and relates to the technical field of software development evaluation or other related technical fields, and the method comprises the following steps: obtaining predefined software development measurement dimensions and measurement indexes, and collecting software development data in a trusted environment; according to a predefined calculation rule, calculating an index value associated with each measurement index in the software development data; according to the index numerical values of all the measurement indexes, performing numerical value scoring on each measurement index to obtain an index score; and based on the index score of each measurement index and the score corresponding to the measurement dimension, generating a software development health degree report according to the report configuration template. The technical problems that a software development measurement mode in related technologies depends on manual operation, and the accuracy of a measurement result is low are solved.

The invention relates to the technical field of artificial intelligence, can be applied to service system platforms of medical health, financial science and technology and the like, and discloses an intelligent operation and maintenance method and device, computer equipment and a storage medium. Acquiring operation data of a target software system in real time, and analyzing whether the operation data contains fault features or not through a pre-trained large language model; when the fault feature is detected, generating a diagnosis result corresponding to the fault feature through the large language model; searching a corresponding target operation and maintenance scheme from the operation and maintenance knowledge base according to the diagnosis result, and performing operation and maintenance processing on the target software system according to the target operation and maintenance scheme; therefore, efficient and accurate intelligent operation and maintenance of the software system can be realized.

The invention discloses a program code management method and device, equipment and a medium. The method is applied to a terminal integrated with a set operation environment and comprises the steps that a construction script configured under a code management directory is obtained, the code management directory is created in advance through a set command under the set operation environment, and a plurality of management subdirectories are further created in advance in the code management directory; analyzing the construction script to generate a code management instruction, and determining a management object corresponding to the code management instruction from each management subdirectory; and running the code management instruction, and executing code management logic for the management object by calling a set function module in the set running environment. According to the technical scheme, the code management directory capable of performing centralized scheduling on the applet codes is constructed, and automatic management on the applet codes is realized by generating the code management instruction.

The invention provides a software development cost evaluation method and device, a medium, equipment and a product. The method comprises the steps of obtaining a demand document corresponding to a target software project; performing paragraph splitting on the demand document to obtain at least one functional process; splitting each functional process into a plurality of sub-processes; identifying keywords in each sub-process, and calculating a CFP score of each sub-process according to a corresponding relationship between the keywords and a preset CFP basic score; and outputting a cost evaluation report about the target software project according to all the sub-processes and the CFP score of each sub-process. According to the method, manual item writing and workload calculation are not needed, the function process and the sub-process thereof can be automatically split and written according to the content in the demand document, the automation degree of the COSMIC method is improved, the complexity of original work is reduced, the time cost is reduced, and meanwhile the accuracy and the normalization of the CFP score of the calculation project are improved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing, to an artificial intelligence model, a request with a prompt that includes contextual information from the software development environment comprising a file containing software code, a project containing the file, a summary of the project, a programming language identifier, a project entitlement, or some combination thereof; in response to the request with the prompt, receiving generative content from the artificial intelligence model that is based on the contextual information, wherein the generative content includes code, a project setting, an asset identifier, or documentation; and presenting the generative content including the code, the project setting, the asset identifier, or documentation, that is based on the contextual information, in the user interface of the software development environment.

The invention discloses a customer service omni-channel-oriented multi-modal arrangement system, and the system comprises an omni-channel access module which integrates ASR, OCR and MLLM technologies and is used for translating multi-modal data input into unified text semantic representation in real time; the logic arrangement module integrates NLP, LLM and IT interface components, supports extension and is used for logic arrangement based on business requirements, and the arranged service has multiple mechanisms and can realize a low-delay and high-availability service chain; the multi-modal generation module integrates TTS, T2I and MLLM technologies and is used for generating corresponding modal data according to channel requirements and supporting streaming and non-streaming output; and the full-process service monitoring module is used for monitoring the state, the time delay and the error rate of each module in real time and guaranteeing the service stability and the user experience. The cross-modal data unified processing is effectively realized, the system development and maintenance cost is reduced, and the customer service efficiency and quality are improved.

A system and method for performing rolling updates of distributed applications in a software-defined network environment. An application manager coordinates deployment of updated versions of fxDeviceApp and fxCloudApp components to isolated execution environments on distinct network elements, such as edge devices and cloud-based containers or virtual machines. During the update process, the system establishes secure communication tunnels between the updated components using a virtual messaging fabric, verifies the integrity of the updated components before activation, and maintains service continuity by activating the new versions only after successful validation. The original application instances are decommissioned after the updated components are fully operational. The system supports dynamic scaling of execution environments based on load conditions, policy-governed update concurrency and ordering, coordinated deployment across multiple zones, automatic rollback on failure, and audit logging of deployment events. These mechanisms enable secure, resilient, and minimally disruptive application lifecycle management in distributed and programmable networking environments.

The invention discloses a project management method and system for artificial intelligence auxiliary software development, and relates to the technical field of artificial intelligence, and the method comprises the steps: employing an artificial intelligence agent, analyzing an original demand document, outputting a conclusion, a score and a modification suggestion of document standard review, and generating a standard demand document; based on a retrieval enhancement generation technology, in combination with a text vectorization core algorithm, vectorizing the specification demand document; retrieving and analyzing similar demands in the vector database by using a similarity algorithm to obtain a demand cost report, feeding back the demand cost report to the artificial intelligence agent, and optimizing the review quality of the artificial intelligence agent by using a multi-target cost embedding algorithm in combination with code positions of similar functions; and based on the optimized artificial intelligence agent, in combination with the result code and the development duration, developing interaction data of a project related party, and analyzing and optimizing the development process of the project. According to the method, the requirement definition, the development efficiency and the overall resource utilization rate are improved.

The invention provides an automatic operation and maintenance platform implementation method. The method comprises the steps that operation and maintenance work responsible for a plurality of operation and maintenance groups is split into basic function units; for each basic function unit, in response to acquisition of a task configuration instruction for the basic function unit, establishing a corresponding atomic task for the basic function unit; based on all the atomic tasks and the operation and maintenance work execution sequence of the operation and maintenance groups, constructing a directed acyclic graph reflecting the execution dependency sequence of the atomic tasks; determining an executable task sequence based on the directed acyclic graph; and according to the executable task sequence, calling a uniform interface to distribute the atomic tasks in the executable task sequence to an agent program for execution. According to the technical scheme, the automation level of the operation and maintenance platform is remarkably improved.

The invention provides a cloud platform operation and maintenance method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-dimensional data of a cloud platform, integrating the multi-dimensional data of the cloud platform into a high-order tensor based on a Tuck decomposition method, carrying out the non-uniform sampling of the high-order tensor, and constructing a high-order tensor structure of the cloud platform; generating a space-time multi-scale topological graph of the cloud platform based on the high-order tensor structure of the cloud platform; constructing a dynamic prediction model of the cloud platform based on the space-time multi-scale topological graph of the cloud platform; preprocessing the multi-dimensional data of the cloud platform, and sending a preprocessing result to the dynamic prediction model for prediction to obtain a state prediction result of the cloud platform in a preset time period; and obtaining a preset operation and maintenance scheme for operation and maintenance based on the state prediction result of the cloud platform in the preset time period. According to the method, the characteristics of long-range dependency and sudden fluctuation are combined, and resource scheduling and anomaly detection are automatically carried out.

The invention discloses an open source component analysis method based on a static program, and relates to the technical field of software analysis. Project source codes are input and analyzed, a semantic calling graph is obtained, and hierarchical management is conducted on the semantic calling graph; carrying out topological sorting on the layered semantic calling atlas to obtain a component check sequence; and comparing the semantic call maps corresponding to the new version and the old version of the to-be-upgraded component, performing software detection operation on the to-be-upgraded component, and outputting a conflict report. According to the method, a layered semantic calling graph is constructed and contains rich context information, so that analysis can go deep into the level of whether behaviors and semantics of the method are changed or not, topological sorting is carried out on component dependency relationships, a component checking sequence is provided, the problem that a conflict is repaired in reality is solved, and the method is more practical. By means of the method, it is guaranteed that before any component is checked, all the components on which the component depends are analyzed and confirmed, and orderliness and certainty of the analysis process are guaranteed.

The invention provides an operation and maintenance system and method based on an MCP protocol and electronic equipment, the system comprises a standardized interface packaging module and a large model context protocol layer service module, interfaces of various operation and maintenance tools are packaged in a large model context protocol layer through the standardized interface packaging module according to a unified packaging standard, and the packaged interfaces are stored in the large model context protocol layer service module; and generating adapters corresponding to the operation and maintenance tools, recording the adapters through a large model context protocol layer service module, obtaining the corresponding adapters in response to an operation and maintenance request of a client, and operating the adapters to execute an operation and maintenance task carried by the operation and maintenance request. Various operation and maintenance tools can be packaged according to a unified packaging standard and then integrated into the operation and maintenance system provided by the application, and when a client initiates an operation and maintenance request, a corresponding adapter is quickly obtained according to the operation and maintenance request, so that plug and play of the operation and maintenance tools is realized, the maintenance cost of an operation and maintenance scheme is effectively reduced, and the operation and maintenance efficiency is improved. And the expansibility of the operation and maintenance system is improved.

The invention discloses a low-code process automatic arrangement method and system, and particularly relates to the technical field of automatic arrangement, and the method comprises the steps: carrying out the unified collection and verification of an external system, resources and system boundaries before deployment, generating an operation environment fingerprint with a signature, and carrying out the strong binding of the fingerprint with a process instance; process DAG modeling is completed on the visual canvas, system and semantic double validity verification is carried out, and a post-curing parameter snapshot is used as a subsequent unique parameter source; in the online simulation period, observable measurement is collected, a deviation perception index is formed, and steady selection and switching of an execution kernel are achieved by combining hysteresis and a minimum residence strategy; in a production state, a canary release strategy, a degradation strategy and a rollback strategy are used for communicating simulation and real disk, and the SLA and the key KPI are continuously monitored; normalized collection and immutable archiving are carried out on structured and semi-structured data in the operation period, an evidence chain and a minimum reproduction experiment package are generated based on hash and signature, and the comparability and traceability of cross-version results are achieved.