1553results about "Software maintainance/management" patented technology

Examples of the present disclosure describe systems and methods for automatically detecting and repairing reliability issues in operating systems and applications using a generative artificial intelligence (“AI”) system. In examples, a generative AI system receives a request to evaluate a detected issue in a software service or application. In response, the system analyzes error information associated with the detected issue to build an error context for the software code that caused the detected issue. The error context is used to identify the location of the software code file that comprises the software code. The error context and the software code file are used to identify a prompt. The prompt, the error context, and / or the identified software code are provided as input to a language model. The language model provides an output that is responsive to the user request and may perform actions to further evaluate or repair the detected issue.

The invention provides an intelligent dynamic prompting method and system for online code writing, and relates to the technical field of codes, and the method comprises the steps: inputting structured data into a pre-trained large language model; analyzing code semantics, business logic and data flow in the structured data through the large language model; identifying potential business fields and data structures based on the analysis result; generating a business field candidate list conforming to the context in combination with specific domain knowledge and coding specifications of the project; performing correlation sorting and screening on the service field candidate list; generating a detailed description of each business field; displaying the generated business field prompt in real time in an integrated development environment of the user; dynamically adjusting the display sequence and content of the business field based on the user interaction behavior; and regularly analyzing the change of the project code library, and automatically updating the knowledge base prompted by the business field.

A data processing system implements obtaining build logs that include information associated with a software build problem; analyzing the logs to generate a knowledge graph identifying the relationship between various entities in the logs; extracting a signature of a candidate root cause of the build problem from the knowledge graph representing a subset of nodes and edges of the knowledge graph; providing the signature of the candidate root cause to a graphical language model to obtain a prediction of a category of root cause failure selected from among a plurality of root cause failures; constructing a prompt for a language model to generate a root cause failure analysis that describes the root cause of the build problem, the prompt including the category of root cause; receiving the root cause failure analysis from the language model; and performing one or more actions in response to receiving the root cause failure analysis.

Various embodiments described herein support or provide operations including receiving input data that comprises a collection of data units; determining that the collection of data units corresponds to a number of tokens that exceeds an upper limit of tokens; processing the collection of data units to generate a plurality of outputs; and aggregating the plurality of outputs into a final result provided as output of a function.

The invention discloses a method and a system for realizing automatic code generation by using multiple AI Agents, and relates to the field of electric digital data processing, a test driving agent receives a task transmitted by a previous node, and generates a task number, a task description, test data, a unit test script and a code statement; the code generation agent generates or updates codes according to the received task description and code declaration; the test driving agent returns an integrated test script and test data according to the task number; the continuous integration agent performs integration verification and regression test and feeds back information; the test driving agent returns a function test script, a performance test script and test data according to the task number; and the continuous deployment agent carries out continuous delivery and deployment and feeds back information. By means of mutual conversation and cooperative work of multiple AI Agents and in combination with a feedback mechanism, the problem that a single large model cannot complete complex software development tasks is solved, full-process operation automation can be achieved, and the code generation quality and efficiency can be improved.

The embodiment of the invention discloses a code defect prediction method, a readable storage medium and a computer program product. The method comprises the following steps: constructing a plurality of prediction models and a plurality of agents; when the code library is updated, determining a plurality of update items, and obtaining new version codes of the plurality of update items; obtaining potential code defect types and potential code defect descriptions of a plurality of update items based on the new version code and the plurality of prediction models; generating defect reports of a plurality of update items based on the potential code defect types, the potential code defect descriptions and the plurality of agents; and carrying out authenticity verification on the defect report, and repairing the new version code after the authenticity verification is passed. According to the method, code defects can be efficiently and accurately predicted, and a defect report is given for repairing related defects, so that software research and development are facilitated.

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 provides a water conservancy industry large model agent application system, and belongs to the field of intelligent water conservancy, and the system comprises a large language model base unit which is used for providing a standard interface to interact with an open source or non-open source LLM, and carrying out the response operation of a user demand; a training data set of the business knowledge base unit is used for accumulating knowledge training LLM, and a knowledge base data set is used for supporting customized demands and expressing customized businesses as knowledge for intelligent agents to call; the water conservancy assembly library unit special for the large model is used for providing a pipe accommodating platform for all assemblies special for the large model; the water conservancy large model service decision support system unit is used for assembling business components into an application system and providing an operation tool to adjust the assembling process to form an applicable system; and the knowledge accumulation unit is used for extracting the system knowledge generated by the water conservancy large model service decision support system unit. The water conservancy application system architecture provided by the invention has complete functions, can be flexible and changeable along with businesses, and is short and long in service cycle.

The invention provides an operation and maintenance method and device based on a large language model, electronic equipment, a storage medium and a computer program product, relates to the technical field of computers, in particular to the technical field of artificial intelligence large models, natural language understanding and operation and maintenance, and can be applied to operation and maintenance scenes. The specific implementation scheme is as follows: iteratively executing the following operations through an intelligent agent: determining a link operation and maintenance result corresponding to a completed link in an operation and maintenance task; through a large language model, according to the link operation and maintenance result corresponding to the completed link, determining a target tool for continuing to execute the operation and maintenance task from the tool set; and calling the target tool, and generating a link operation and maintenance result corresponding to the next link in the operation and maintenance task. According to the method and the device, the Agent Loop (agent loop) mode is adopted, the target tool of the next link in the operation and maintenance task is determined according to the link operation and maintenance result corresponding to the completed link in the operation and maintenance task, and the applicability of the operation and maintenance scheme in each operation and maintenance scene and the accuracy of the operation and maintenance result are improved.

A software application includes work order resources, each of which defines an atomic operation for the software application, and a construction service resource, which processes the work order resources in response to all interaction requests for the software application. Each interaction request is received from a client and identifies a corresponding work order, which the construction service processes to dynamically construct a set of deliverables, which can include a custom representation of the work order. While processing the interaction request, the construction service, as directed by the work order, can make one or more requests to context resources for context information corresponding to an activity for which the interaction was requested to construct the set of deliverables. The work order resource can comprise a reflective program that enables the construction service to dynamically determine and construct the set of deliverables, including the next appropriate interaction(s) using the context information, thereby directing a set of atomic operations as part of an activity being performed and enabling the dynamic context-based construction of interaction deliverables.

Systems, methods, and computer program products are provided for providing coding change review. The method includes receiving a code change indicator. The code change indicator is associated with a changed code that has been changed by at least one user. The method also includes determining a coding language of the changed code based on at least one of a keyword library or a code pattern. The method further includes determining a change review indication based on the determined coding language. The change review indicator indicates whether one or more errors exist in the changed code. The method still further includes determining one or more change review actions to carry out based on the change review indication.

The invention discloses an interface element positioning method and device, and the method comprises the steps: obtaining an image when an interface is loaded for the first time, taking the image as a reference image, carrying out the feature extraction and recording of the reference image through a trained element positioning model, and taking the extracted interface element as a reference interface element; acquiring an image when the application runs or the interface is updated and taking the image as a current image, performing feature extraction and recording on the current image by utilizing the trained element positioning model, and taking the extracted interface element as a current interface element; if it is determined that the feature information of the current interface element changes relative to the feature information of the reference interface element, an identification result is output; and according to the identification result, updating the feature information of the reference interface element as the feature information of the current interface element, and updating the corresponding test script to position the interface element. By adopting the method and the device, element positioning can be dynamically identified and adjusted during test operation, and frequent maintenance requirements are reduced.

The invention relates to a self-adaptive plug-in architecture optimization method and device based on Q-Learning, and the method comprises the steps: defining a state space, an action space and a reward function of a target object, and initializing a mapping table of states and action values; obtaining a preset action selection strategy, and determining and executing a target execution action according to the action selection strategy; obtaining running state information after the target object executes the target execution action, and determining an instant reward value according to the running state information and the reward function; and acquiring a preset action value updating strategy, and iteratively determining the updated action value according to the action value updating strategy, the instant reward value, the state space and the action space until the change amplitude of the updated action value is not greater than a preset amplitude threshold. By adopting the method, the requirement of plug-in configuration can be automatically adjusted according to the change of the system state and the service requirement, so that the stability and the reliability of the system are improved.

The invention relates to a code generation method based on a multi-agent self-reflection framework, which combines a staged optimization process of prompt generation, test case generation, code generation, result check and code repair, and effectively solves the technical problem of unstable code generation quality in the prior art. By introducing a pruning method to perform quality screening on output of each stage and utilizing an intelligent feedback mechanism to realize efficient error repair and iteration, the code generation accuracy, reliability and efficiency are remarkably improved, and an innovative and efficient technical solution is provided for intelligent code generation of complex programming tasks.

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 relates to the technical field of data processing, in particular to a cloud computing-based software development application data processing method, which comprises the following steps of: collecting original data generated by software development application from a plurality of heterogeneous data sources in real time through a distributed data acquisition module; cleaning, filtering and formatting the original data, removing noise data and redundant information, and generating a standardized data set; the standardized data set is fragmented according to a preset fragmentation strategy; analyzing the stored data in real time by using the parallel processing capability of cloud computing to generate an analysis result; a result storage and updating step; a data visualization step; a security and authority management step; and an anomaly detection and fault-tolerant processing step. According to the method, the data processing speed and the throughput of the system are improved, and the requirement for real-time feedback in an agile development environment is met.

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.

The invention discloses a multi-agent driven industrial software component assembly method and system, and the method comprises the steps: obtaining a subtask sequence corresponding to a target task instruction and a first component sequence corresponding to the first subtask sequence; according to the target task instruction, performing regeneration optimization on the first subtask sequence and the first component sequence to obtain a second subtask sequence and a second component sequence; querying a historical task log corresponding to the second subtask sequence and the second component sequence; according to a historical task log, performing regeneration optimization on the second subtask sequence and the second component sequence to obtain an optimization response representation; and according to the historical task log, performing parameter reasoning on the optimization response representation to obtain a target task workflow. According to the assembly method, the development time consumption of the task workflow can be reduced, the working intensity of workers is reduced, and the generation quality of the task workflow is improved. The invention relates to the technical field of deep learning.

The invention provides a security analysis method in a software research and development process. The security analysis method can be applied to the technical field of big data and the technical field of artificial intelligence. The method comprises the following steps: embedding a security analysis plug-in in an integrated development environment; scanning codes written by a user in the integrated development environment in real time through safety rule information preset in the safety analysis plug-in and a heuristic algorithm; identifying a target security problem in response to the security analysis plug-in, marking the target security problem by using the security analysis plug-in, and providing repair suggestion information; and responding to the condition that the user modifies the code based on the repair suggestion information, and utilizing the security analysis plug-in to re-scan the modified code to verify the modification effect.

The invention belongs to the technical field of computers, and particularly discloses a method and system for integrating a code structure into a large model based on multiple modalities, and the method comprises the steps: obtaining a code attribute graph corresponding to a code text; inputting the code attribute graph to a graph encoder, and obtaining a graph structure representation output by the graph encoder; the code text and the graph structure representation are input to a large language model, a code processing result output by the large language model is obtained, and the large language model is used for processing the code according to the code processing task based on the code text and the graph structure representation; wherein the graph structure representation and the serialization language representation are kept aligned in a representation space, and the serialization language representation is obtained by performing serialization processing on a code text by a text encoder in a large language model. According to the method, the multi-modal encoder is introduced, so that different modal information, such as grammar, semantics, control flow and data flow, of the code can be fused and processed, and the understanding ability of a large model to a code structure can be remarkably improved.

The invention relates to the technical field of data mining, in particular to a software operation and maintenance method and system. The method comprises the following steps: acquiring engineering simulation heterogeneous hardware data, and performing multi-physics field coupling tensor compression to obtain a multi-physics field topology snapshot; finite element grid nonlinear constraint is executed based on the multi-physics field topology snapshot, and a nonlinear contact strategy tensor is obtained; according to a non-linear contact strategy tensor coding structure dynamics intelligent contract, simulation software dynamics credibility verification is carried out, and a dynamics credible chain is obtained; constructing a hydrodynamic vortex model based on the dynamic trusted chain, and performing boundary layer separation simulation flow optimization on the hydrodynamic vortex model to obtain a vortex flow field map; performing explicit dynamic explicit integration on the vortex flow field atlas to obtain a convergence optimization field; and performing phase field damage evolution according to the convergence optimization field to obtain a multi-physics field evolution twinborn body, and deploying the multi-physics field evolution twinborn body. The method can effectively improve the utilization rate of software computing resources.

The invention discloses a software development system based on an artificial intelligence technology, and relates to the technical field of software development. A software development system based on an artificial intelligence technology comprises a user demand analysis module, a self-consciousness module, an automatic code generation module and an intelligent feedback optimization module. Human intelligence is simulated through the self-consciousness module, changes in the development process are perceived in real time, and self-learning and adjustment are carried out according to historical data and real-time feedback; the adaptive capability enables the system to automatically optimize the development strategy according to different requirements, technical changes and development progress, ensures that the software development process can flexibly cope with various dynamic changes, and avoids frequent manual intervention caused by requirement changes or technical bottlenecks in the traditional development process.

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 relates to the technical field of software development, and provides a method and a system for generating a software development scheme. The method comprises the following steps: S1, acquiring a user demand, extracting a functional demand and a non-functional demand, and processing; s2, generating a system architecture design based on a demand analysis result, and selecting a proper technical scheme in a manner of combining a collaborative filtering model and reinforcement learning; s3, based on the architecture design and the demand analysis result, a development task is automatically generated, and resource scheduling is dynamically adjusted; and S4, generating an automatic test based on a demand, and performing intelligent analysis on a test result. And dynamically generating system architecture design and optimizing technical stack selection through a method of combining a collaborative filtering model and reinforcement learning. Collaborative filtering is associated with implicit vectors of a technology stack through user requirements, reinforcement learning optimizes decisions according to historical experience, expandability of architecture design and reasonability of technology type selection are guaranteed, different project requirements are met, and system performance is improved.

A method, system, and non-transitory processor-readable medium for optimizing lifecycle rule computer processing including: selecting a set of lifecycle rules to apply to interactions, finding a set of interactions satisfying criteria associated with the lifecycle rule; calculating an associated rule occupancy score based on the number of executed interactions associated with the rule, and executing the rule based on the rule occupancy score associated with the rule.

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.