474results 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.

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 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.

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.

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 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.

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 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 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.

Disclosed herein are methods, systems, servers, and computer-readable media for interpreting computer code with a multimodal machine learning model. In an embodiment, this comprises: receiving, an input comprising at least one of a text prompt, file prompt, or data object, determining, using a multimodal machine learning model, that the input requires implementing computer code, and in response to determining the input requires implementing computer code: generating computer code based on the input, executing the generated computer code using a code interpreter, and providing, through an interface, an output based on the generated computer code.

The invention provides a method and device for code analysis, electronic equipment, a computer readable medium and a computer program product. The method comprises the following steps: performing code analysis on a target code to obtain multi-dimensional structured information of the target code; converting the obtained structured information into context information which can be understood by a large language model; performing semantic understanding and intention inference on the context information by using a large language model; and generating analysis result information of the target code based on the semantic understanding and intention inference results. According to the method, through multi-dimensional code analysis, grammar analysis and deep semantic understanding are remarkably improved, and the generation quality of a code analysis document is greatly improved; code logic and design intention are deeply understood by using a large language model, high-quality technical documents are automatically generated, and the intelligent level of code analysis is remarkably improved.

Disclosed are systems and methods for automatically detecting faults in software applications or hardware components. The faults are characterized according to the effect, or underlying cause of the fault, or another criteria. The system automatically selects or generates a correction for the faults that is sent to the computing device where the fault began as part of correcting the fault. The fault can be characterized or the correction can be generated using machine learning technology.

The invention discloses a development management system and method for an API interface, and relates to the technical field of software engineering. The system comprises: an intention understanding and design engine, which analyzes a multi-modal service demand into a structured API development task; the agents collaboratively develop a network, and the multiple AI agents are dispatched to generate API specifications, codes, test cases and deployment configurations in parallel; the API ecological dynamic map construction module is used for automatically constructing and updating a dynamic map of dependency and data flow direction between APIs based on a generated product; a continuous learning and evolution engine analyzes the atlas and operation data to identify optimization points and generate or execute evolution policies. All the modules form a closed loop, and output of the evolution engine is fed back to the intention understanding engine to optimize subsequent design. The corresponding method comprises the steps of demand analysis, intelligent collaboration, atlas construction and closed-loop optimization. According to the method, automatic development, intelligent collaboration and continuous architecture optimization of the API are realized, and the development efficiency and the system maintainability are improved.

Techniques are provided for failure mitigation in software deployment pipelines using generative artificial intelligence (AI). One method comprises obtaining a request to merge code changes associated with a first branch of software code with a second branch of the software code; in response to the request: obtaining information characterizing the software deployment pipeline; applying at least a portion of the information to a classification model to obtain a prediction that an implementation of the request will result in a failure; applying, in response to the prediction that the implementation of the request will result in the failure, at least a portion of the information to a generative AI model, with failure information characterizing reasons for the failure, to obtain mitigation actions to mitigate the failure; and automatically initiating processing steps associated with at least one of the mitigation actions to mitigate the failure.

Disclosed is a method (500) for adjusting Service Level Agreement (SLA) parameters in a communication environment (100). The method includes collecting 5 data associated with workload, resource utilization, and performance metrics. The method further includes setting one or more SLA parameters for one or more services included a SLA. Further, the method includes predicting a future workload pattern, resource requirements, and future SLA breaches by analyzing the collected data. Furthermore, the method includes evaluating a performance of the one or more 10 services against SLA target and the future workload pattern. Furthermore, the method includes adjusting the SLA by adjusting the one or more SLA parameters in real-time to maintain a consistent service level in the communication environment.

A method and apparatus are disclosed for enhancing operable functionality of input source code files from a software program by identifying a first code snippet and a first library function which have similar execution flow similarity measures by evaluating a first contiguous sequence of n items extracted from the first code snippet against a second contiguous sequence of n items extracted from the first library function using an Ngram threshold that is calculated for the first library function and one or more literals from the first code snippet so that the developer is presented with a library function recommendation which includes the first code snippet, the first library function, and instructions for replacing the first code snippet with the first library function.

Various embodiments of the present disclosure include systems, methods, and / or apparatuses for executing an artificial intelligence (AI) framework. The system may be configured to: receive an initial software functionality definition data set; retrieve at least one configuration data set comprising predefined context data; generate, at least by at least one agent executing at least one generative AI model, one or more requirement data objects for a computer-generated executable software program product based on the initial software functionality definition data set; generate one or more code generation work prompt sets defining a list of computer-executable tasks configured to generate code for the computer-generated executable software program product; and / or build the code for the computer-generated executable software program product.

A code processing method, comprising: receiving a first command, the first command being used for instructing to mark runtime information on first source code; acquiring runtime information of a first application program, the first application program being obtained by compiling on the basis of the first source code; reading the first source code from a memory, and marking the acquired runtime information on the first source code, to obtain second source code; and storing the second source code in the memory. In this way, by marking the runtime information on the source code, subsequent developers can directly view related runtime information from the source code, and make generalization modifications on the basis of their own requirements, so as to improve generalization. In addition, the runtime information is marked on the source code. Therefore, after the source code is upgraded and the source code changes slightly, the source code marked with the runtime can be directly used to compile a program. In this way, no additional performance analysis data needs to be obtained after the source code is upgraded, thereby improving code development efficiency.

The application relates to the technical field of system integration management, and discloses an application system integration management system and method based on artificial intelligence. The system obtains integration requirements of application systems through an information acquisition module; a compatibility analysis module analyzes compatibility between systems to generate a preliminary integration scheme in combination with the integration requirements and a pre-trained artificial intelligence model; a preliminary configuration module configures data conversion rules and interaction protocols between systems according to the scheme, and outputs a preliminary configuration result; an integration acquisition module completes preliminary integration based on the configuration result, synchronously acquires state data and abnormal information in the integration process; a state feedback module feeds back the data to the artificial intelligence model; and a scheme optimization module optimizes the preliminary scheme by means of the model after feedback, and obtains an optimized integration scheme. The application effectively solves the problems of low efficiency, poor accuracy, insufficient integration stability and high maintenance difficulty caused by the lack of real-time feedback and dynamic optimization in the prior art, which are caused by the fact that application system integration relies on manual operation.

The invention discloses a machine room safety operation and maintenance method and system based on a large model operation and maintenance assistant, and belongs to the technical field of machine room operation and maintenance, and the method comprises the steps: collecting machine room data, and carrying out the safety processing, specifically, carrying out the privacy calculation of to-be-processed data through a constructed double-layer safety credible environment, and carrying out the privacy calculation of the to-be-processed data; virus discrimination and pollution data isolation are carried out in the privacy calculation process; optimizing the large model of the operation and maintenance assistant; using the optimized large model to generate operation and maintenance question and answer data, and performing security processing on interaction data in a question and answer process; in the operation process of the operation and maintenance assistant large model, an NPU storage encryption engine is adopted to carry out dynamic encryption storage on storage data of the large model, and the influence of the NPU storage encryption engine on memory access characteristics in the operation process of the large model is quantitatively analyzed through a simulation platform. The operation and maintenance automation level of the machine room can be guaranteed, data safety is guaranteed, the performance of an operation and maintenance assistant is improved, and the stability and reliability of an operation and maintenance system are improved.

Method for operating a Large Language Model, LLM agent, wherein an operating system of a computing device executes an LLM agent stored in the computing device, as well as LLM device and computer program product.

The invention discloses a public data fusion development platform which comprises a platform portal website, a business center, a capability support plate and an infrastructure layer. The platform web portal serves as a user layer to provide an external service window; the business center serves as a business layer and comprises an operation management system, a second-level development system and a competition management system which serve a first-level development subject, a second-level development subject and the public respectively. The capability supporting plate serves as a middle table layer and comprises a data management system, a resource management system, a privacy computing system and an operation and maintenance management system which are used for providing data management, resource scheduling, security computing and full-life-cycle operation and maintenance support for the service center; the infrastructure layer provides one or more infrastructures including a cloud resource pool, a privacy computing environment, a computing engine, containerization, a scheduling engine, intrusion protection, threat detection, a firewall, and a bastion host for the capability support section.

This application provides a software development management method, apparatus, and software development system. The method includes: a compilation step, compiling a development baseline to obtain the development baseline of the main project team and the development baseline of the collaborating project team; a merging step, merging the development baseline of the main project team and the development baseline of the collaborating project team to obtain a merged development baseline; a tracking step, tracking the development progress of the merged development baseline after a predetermined development period based on the merged development baseline, obtaining a progress tracking result; an updating step, updating the merged development baseline based on the progress tracking result, obtaining an updated merged development baseline; and a repetition step, repeating the tracking step and the updating step at least once until the software development is completed. This method solves the problem of low efficiency in software development in the prior art.

The application discloses a kind of low-code application quick construction method, including the following steps: data preparation, application construction, application release, construction is completed, finally generates application module;First, data preparation is carried out, obtains the construction resource data required for application construction, is compressed and uploaded to construction module, then construction module decompresses data and passes through visual construction page, business logic and data structure, to design page design application function in the way of drag and drop.By this method, non-professional personnel can also directly build the application module that oneself needs, improve the application building speed and user-friendly.

The invention relates to a cross-platform digital intelligent workbench system without browser dependence. The system comprises a cross-platform shell module, a native capability module, an embedded webpage rendering engine module, a browser function management and control module, a unified message driving module, an access adaptation module and a construction and release module. A C + + + cross-platform shell hybrid architecture is adopted, a set of codes can generate a Windows / macOS / Linux deployment package through multi-target compilation, and the multi-terminal maintenance cost and the publishing complexity are remarkably reduced; a front-end page is directly rendered in the client side through an embedded WebView; rendering, network, downloading and other capabilities depending on an external browser in history are disassembled into sub-modules controlled by a strategy, so that jump-out is avoided, environmental requirements are reduced, and Cookie / cache and authority control boundaries are unified.

The invention discloses a method for explaining an error alarm. The method comprises the following steps of: presenting description information of a plurality of alarm records output by analyzing a code file of a detected item to a user; according to the description information of the multiple alarm records, extracting target code snippets associated with the multiple alarm records and rule description of a target rule; retrieving at least one of a description text of a defect code, a description text of a misinformation scene or an example code snippet identified by the target rule from a knowledge base according to the rule description; according to the description text of the defect code, the description text of the false alarm scene or the example code snippet and the rule description and the target code snippet of the target rule, generating a false alarm conclusion and reason explanation of the multiple alarm records through a language model; and presenting a false alarm conclusion and reason explanation to the user. According to the method, the rule check principle is interpreted through the language model, the program execution process is analyzed and deduced, the reason explanation for generating the misinformation is generated, evidence is provided for the misinformation conclusion, and high availability and reference are achieved.

Annotating changes in software across computing environments, including: monitoring a software development and deployment pipeline; annotating a detected change in software with evidence providing information about one or more aspects of the detected change in software; and referring to the annotations for an unexpected change upon detecting the unexpected change in an environment that is outside of the software development and deployment pipeline.

The invention provides a request processing method and device, and the method comprises the steps: responding to a received current code merging request associated with a target branch, and obtaining the states of a plurality of code merging entrance guards associated with the current code merging request; on the basis of the weight and the state corresponding to each code merging entrance guard, determining a merging readiness score of the current code merging request; in response to the fact that the merging readiness score is smaller than a set score threshold value, determining a target access control with the maximum weight from at least one unpassed code merging access control; according to the access control failure log associated with the target access control and the first incremental code difference between the source branch and the target branch associated with the current code merging request, merging blocking information is generated and visually displayed, and therefore the automation and intelligence level of the code merging process is improved, and the code merging efficiency is improved. And the problem repairing period is shortened, so that the stability and delivery efficiency of the target branch are improved.