770results about "Requirement analysis" patented technology

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

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The invention provides a script preservation method which can be applied to the technical field of artificial intelligence. The script preservation method comprises the following steps: carrying out semantic understanding on demand related files by utilizing a large model, extracting core elements, and generating a structured demand change list; analyzing to obtain an abstract syntax tree of an existing script code, and constructing a mapping relationship between the software automation test element and a script code block; dynamically comparing the structured demand change list with an existing script code structure tree based on a mapping relation between software automation test elements and script code blocks, identifying a script range influenced by demand change, and outputting a change influence matrix; and based on the change influence matrix, generating an automatic script code snippet needing to be updated, and realizing preservation of the automatic script. The invention further provides a script preservation device and equipment, a storage medium and a program product.

The invention discloses a code generation and restoration method and device based on multi-round feedback of a large language model, and the method comprises the following steps: 1, converting problem description into a structured tetrad (target, input, output and constraint) through a two-stage task demand deconstruction mechanism, and generating a pseudo-code frame according to the structured tetrad; 2, generating a plurality of candidate code schemes in parallel based on different implementation strategies; 3, selecting an optimal code implementation scheme through multi-dimensional evaluation; 4, generating an extended test case through boundary condition analysis and public test reasoning, and expanding a verification range; 5, classifying error types in combination with static analysis and dynamic execution information, and applying a special repair strategy; 6, providing detailed repair reasons and ideas by an interpretable repair mechanism; and 7, establishing a multi-round feedback repair verification iteration mechanism composed of testing, analysis, repair and verification. According to the method, the performance of an existing large language model in processing a complex programming task is effectively improved.

According to the automatic software development method and device and the computer program, intelligent demand analysis is achieved through a natural language processing technology, dynamic task arrangement is conducted through a finite state automaton, collaborative development is completed by means of a multi-specialized role agent, intelligent verification is implemented based on a template matching mechanism, and the development efficiency is improved. And a complete automatic closed loop from demand to delivery is constructed. According to the scheme, full-process automation is realized, manual intervention is greatly reduced, and the development efficiency is remarkably improved; through division and cooperation of multiple agents, the professionality and decision-making quality of each link are ensured, and the capability limitation of a single agent is overcome; flexible and reliable process control is provided based on state machine management, and development state changes are dynamically adapted; and the standard consistency of the delivery result is ensured by combining template verification. According to the method, the problems of long development period, large quality fluctuation and the like caused by chain splitting, low intelligent level and excessive dependence on manpower of an existing development tool are effectively solved, and a feasible path is provided for comprehensive intelligence of software development.

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

The invention provides a UI interface automatic generation method and system based on natural language description, and the method comprises the steps: receiving a natural language demand description text, carrying out the guided completion of fuzzy description in the demand description text, and outputting a standardized demand text; semantic analysis is carried out on the standardized demand text, functional association nodes of UI components and data fields in the text are positioned, analysis deviation is corrected through a domain knowledge graph, and key information including component parameters, field verification rules and component priorities is obtained; calling a design specification library comprising a sub-scene visual standard and a dynamic layout algorithm, and a component library comprising a parameterized configuration function; performing component matching and parameter adjustment based on the key information, a design specification library and a component library to obtain a target component, and generating a front-end code or a low-code blueprint of the target component; and rendering to obtain a UI interface. According to the method and the device, the problems of low demand transmission efficiency, high development threshold and difficulty in multi-terminal adaptation in the prior art are solved.

The invention discloses a life omics knowledge graph construction method and system based on a large language model, and relates to the technical field of computer data processing. The method comprises the following steps: acquiring and preprocessing multivariate life omics data, wherein the multivariate life omics data at least comprises an unstructured biomedical text; performing information extraction on the text data based on a large language model to obtain entity mention and relation description; standardizing and normalizing the entity mention and the relation description on the basis of a large language model in combination with an external knowledge base to obtain a standard knowledge triple; and storing the triple into a graph data storage system, and constructing the knowledge graph. According to the method, the powerful natural language understanding ability of the large language model is utilized, efficient information extraction is achieved through structured prompt or field fine tuning, the model is innovatively utilized for entity standardization of relation perception, and the accuracy of knowledge fusion is remarkably improved.

The invention relates to the technical field of computers, and discloses an application development method based on a large model technology, and the method comprises the steps: 1, carrying out the demand analysis, and converting a fuzzy business description into an executable technical scheme through natural language understanding and a structured modeling technology; 2, carrying out architecture design and planning, and constructing a code-free and low-code dual-mode collaborative architecture; 3, establishing a visual arrangement engine, and establishing a dual-mode development interface of a code-free development mode and a low-code development mode; step 4, aiming at different requirements of a code-free scene and a low-code scene, a dynamic adaptation strategy is adopted, and differentiated resource management of lightweight adaptation and elastic expansion is carried out; and step 5, performing field adaptation, and obtaining the industry exclusive capability according to the general model and the field knowledge through code-free rapid adaptation and low-code deep customization. By means of the scheme, the development threshold can be lowered, the efficiency can be improved, and the accuracy of cross-industry application is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a demand-driven data processing method, device and equipment and a medium, and the method comprises the steps: obtaining demand information, and generating a structured demand analysis report through a demand analysis agent; generating an executable code by combining a strategy conversion agent with a zero code operation platform; generating a test case through the automatic test agent and executing an automatic test; when the test result is wrong, adjustment is carried out through the result feedback agent; when the test result is passed, executing the executable code to generate a processing result, and verifying the intelligent experience certificate by the accuracy; and storing the verified processing result, and performing multi-level data analysis through the data analysis agent to obtain an analysis result. By establishing a closed loop of demand analysis, rule conversion, test verification and data analysis, rapid conversion and accurate verification from the demand to the result are realized, the processing efficiency is improved, and the error risk is reduced.

The invention relates to the technical field of software engineering and information processing, in particular to a semantic verification method and device based on a large model.The method comprises the steps that a source code file and a corresponding software requirement and design document are obtained, code semantic representation is generated based on grammar analysis and static program analysis, and a semantic verification result is obtained; based on text analysis and business flow chart structure identification, generating a design demand semantic representation; respectively inputting the code semantic representation and the design demand semantic representation into a pre-training language large model to generate a code implementation logic description and a design demand logic description; and performing semantic consistency verification on the two types of logic descriptions to obtain a semantic inconsistency result, determining a code position in the source code file based on the semantic inconsistency result, and outputting a scanning report. According to the invention, the automation degree and the rechecking performance of consistency checking of requirements can be improved, and the leak detection and reworking cost can be reduced.

The invention relates to the technical field of data processing, in particular to a complex semantic-oriented business process multi-agent modeling and intelligent error correction method and system. The method comprises the following steps: firstly, based on a BPMN meta-model, disassembling business process elements, analyzing BPMN specifications, forming a structured framework of business process modeling, processing unstructured texts in parallel through an intelligent agent group, identifying business process key concepts, mapping the business process key concepts into semi-structured information, and generating a preliminary extraction result; verifying the integrity of the preliminary extraction result based on a BPMN specification, and generating an information integrity verification report; based on an information integrity verification report, missing information of the preliminary extraction result is retrieved through a knowledge base and complemented; performing secondary verification on the complemented extraction result, and correcting the complemented extraction result based on business logic and user requirements; and generating an XML code and a visual flow chart conforming to the BPMN specification based on the corrected extraction result. According to the method, the problems of semantic fracture and structure deficiency of the complex semantic text in business process modeling can be effectively solved.

According to the complex nested SQL generation method based on KG-LLM two-way reasoning, a framework of'knowledge graph (KG) reasoning path constraint and large language model (LLM) semantic generation optimization 'is adopted, it is ensured that SQL statements conform to a database structure by means of deterministic constraint of KG, complex nested query is flexibly processed by means of semantic understanding capacity of LLM, and the method has the advantages of being simple in structure, high in efficiency and high in efficiency. The defect of flexibility of an existing method is overcome, and diversified query requirements and dynamically changing query logic can be flexibly coped with; besides, through an information interaction mechanism of KG reasoning and LLM reasoning, core constraint information of KG is injected into LLM to generate context, SQL fragments generated by LLM are verified and subjected to conflict resolution, it is ensured that generated SQL statements conform to database structure constraints and also conform to user intentions, and the user experience is improved. The problem that accuracy and reliability are limited when complex SQL statements are generated through an existing method is effectively solved.

A system and method for generating structured software application specifications through multi-phase conversational dialogue is disclosed. The system implements multiple specialized AI agents including a framework generation agent, an interactive coaching agent, specialized capsule agents for analyzing different concern categories, and a specification coaching agent. During Phase 1, the system conducts exploratory dialogue using a tailored question framework while detecting and storing user concerns in structured capsule entries. Concern injection into the dialogue is strategically timed based on algorithmic evaluation of cooldown periods and user sentiment analysis. During Phase 2, the system conducts comprehensive concern resolution dialogue and generates a final specification in structured JSON format with explicit traceability linking requirements to source conversations and concern resolutions. The system solves technical problems of preserving concern context across conversation phases, optimizing injection timing to avoid overwhelming users, and generating machine-readable specifications suitable for automated downstream processing.

The invention discloses an automatic multi-source analysis and integrated processing system based on artificial intelligence, and belongs to the field of software automation. Comprising a multi-source input unit, a unified semantic modeling unit, an automatic analysis unit, an automatic preprocessing unit, an automatic collaborative execution unit and a security authentication mechanism, wherein the security authentication mechanism comprises primary security authentication and secondary security authentication. According to the invention, end-to-end automatic closed loop from demand understanding to autonomous analysis of the processing path to execution landing can be realized, and key appeals in a complex project business scene can be accurately responded.

The invention discloses a demand development method, device and equipment. The method comprises the following steps: calling a demand analysis agent to analyze a natural language demand into a structured function demand unit comprising an input specification, an output specification and an execution constraint; the calling code generation agent generates source codes corresponding to the development requirements according to data type definitions and function calling specifications in the structured function requirement unit and the local project code library; calling a code test agent to generate and execute a test case based on the source code and the structured function demand unit; and after determining that a software deployment condition is met based on a test result, calling a code deployment agent to generate a deployment script and configuration information, and submitting the deployment script and the configuration information to a deployment execution engine to complete automatic deployment. According to the application, end-to-end automatic development from natural language requirements to application deployment can be realized.

The embodiment of the invention discloses a process template generation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a process generation demand of a user, and determining structured process logic description information based on the process generation demand, a preset process design cue word and a preset large language model; generating cue words and a preset large language model based on the process logic description information, a preset process template, and determining a first process template required by the user; performing rule verification on the first process template based on a preset verification plug-in, and determining a verification result; and if the verification result is that the verification is not passed, adjusting the first process template by utilizing a preset large language model, and determining a second process template required by the user. Through the technical scheme of the embodiment of the invention, the executable process template can be directly generated according to the demand described by the natural language, the node attribute and the logic relationship do not need to be manually configured, and the generality, the efficiency and the accuracy of the process template generation are improved.

The invention relates to a machine question answering technology, in particular to a code generation and correction method based on multilevel context awareness and memory iteration, which comprises the following steps of: S1, receiving a preliminary fuzzy demand, proposing a clear question and generating a structured demand document according to content supplement and question answer; s2, learning the context of the item and generating an initial code according to a learning result and the structured demand document; s3, running the initial code; s4, monitoring operation, capturing logs, if the operation is successful, ending circulation, and if the operation is not successful, entering S5; s5, analyzing the log and positioning errors; updating the error to a problem pool and a correction history, and triggering a correction request; the positioning error comprises extraction abnormity, files and line numbers; the correction request comprises an error code snippet, error information and a correction history; and S6, analyzing the correction request and generating a correction code, wherein the correction code comprises construction of an enhanced diagnosis prompt word. According to the invention, rapid, efficient and accurate project code generation can be realized.

The invention relates to the technical field of computer software, and discloses a low-code-driven end-side cloud collaborative intelligent development and operation system and method, and the system comprises a low-code modeling module, an end-side cloud intelligent operator library, a resource sensing module, a monitoring module, an optimization module, an intelligent scheduling module and a deployment module. The low-code modeling module converts the operation information into a task description file; the intelligent scheduling module determines an initial execution position of each target operator in the task description file based on the task description file; calling an initial target execution operator corresponding to each target operator; the optimization module determines the final operation position of each target operator based on the computing resource information of the current terminal, the edge end and the cloud end; the deployment module calls a final target execution operator corresponding to each target operator; and the final target execution operator is allocated to the corresponding final operation position, so that migration and optimization of the end, the edge and the cloud can be dynamically performed, and a cross-layer application can be quickly constructed.

The invention discloses an LLM (Logical Language Modelica) domain method for Modelica intelligent modeling optimization, and belongs to the field of computer-aided modeling. Structured information is converted and extracted by collecting field data of the new energy automobile; then, based on the RAG technology, intelligent blocking and vectorization processing is carried out on the structured information to form a structured knowledge base, and then a recall test is carried out by calculating a comprehensive weighted score to judge whether the structured knowledge base is available or not; then, the LLM is integrated into an AI-Agent framework, an AI-Agent is formed, and LLM parameters are configured; integrating the structured knowledge base which passes the test into the AI-Agent by utilizing an API (Application Program Interface) and an access protocol; and meanwhile, a specialized cue word template is designed and integrated into the AI-Agent, and a Modelica code generation template and related constraints are standardized. And finally, the user asks a question to the AI-Agent, the LLM works according to a specialized cue word instruction, an API interface is called to access the knowledge base, and Modelica codes which conform to language specifications and can be operated by the user are automatically output through the LLM. According to the invention, end-to-end conversion from unstructured input to high-fidelity model output is realized.

The embodiment of the invention provides a production process configuration method, system and device, a storage medium and a program product, and relates to the field of artificial intelligence. The method comprises the following steps: after obtaining a service demand, analyzing the service demand based on an intelligent agent, and generating corresponding configuration information in combination with a configuration information management rule; in the code development process, the configuration information is adjusted based on the agent according to the change information of the code; and after code development is completed, packaging the latest code according to a preset version management rule based on the intelligent agent, generating a version package, and deploying the version package to a corresponding environment. According to the method, the configuration information is automatically generated according to the service requirements, the configuration information can be automatically adjusted according to needs in the development process, and after the codes are completed, the codes can be automatically packaged and deployed into the corresponding keys, so that developers only need to provide some key service requirements, and the development efficiency is improved. Manual operation links in the production process are reduced, the production efficiency is improved, and the error rate is reduced.

The invention provides a man-machine conversation-based software cost intelligent evaluation interaction method and system, and relates to the technical field of software cost evaluation, and the method comprises the steps: obtaining project description information, carrying out intention recognition and entity extraction, and carrying out semantic matching in combination with a knowledge graph to obtain candidate cost references; according to the method, the estimation reference value is generated based on multi-dimensional similarity calculation and weight self-adaptive adjustment, user feedback correction is supported, accurate evaluation and dynamic adjustment of the software cost are achieved, and the accuracy, transparency and efficiency of software cost evaluation are improved.

The present disclosure of the various embodiments relates to using a large language model to assistant with the creation of secure code and / or the completion of threat modeling tasks in software development. In one example, a system comprises a computing device configure to identify a prompt that requests generating secure source code for source code with a security vulnerability. A security data source is queried for a security threat embedding. The security threat embedding is received from the security data source and an augmented prompt is generated. The augmented prompt is transmitted to the large language model. A secure source code is received from the large language model and imported into application source code in a software development environment.

The invention discloses a code-free application construction method and device based on a multistage service model, and relates to the field of code-free application construction. The method comprises the following steps: receiving a service demand of a user to obtain a demand feature tag; based on the demand feature tags, matching service models meeting conditions to obtain a multi-stage service model set; analyzing and instantiating the multi-stage business model set through a generation engine, and outputting an initial application component; receiving a configuration instruction of a user, and adjusting the initial application component to obtain a target application component; calling a data bridging interface, a permission matrix interface and a cross-component collaboration interface, and establishing a data flow relationship and a permission mapping rule among the components in the target application component to obtain an initial application and integrated link; and verifying the initial application, if the verification result meets the application release condition, deploying the initial application to the target operation environment, and outputting the target application. By implementing the technical scheme provided by the invention, the application construction efficiency and stability can be remarkably improved.

The invention relates to a low-code development method and system based on a large language model and a medium, and relates to the technical field of software development. The method comprises the steps of obtaining a user input instruction, and extracting a natural language instruction in the user input instruction; based on a natural language processing engine, performing intention recognition and entity extraction on the natural language instruction to obtain structured query information; importing the structured query information into a trained large language model to obtain response information; according to the method provided by the embodiment of the invention, the large language model is deeply integrated into the low-code development process, so that the use threshold is remarkably reduced, the development efficiency is greatly improved, and the flexible customization capability is enhanced.

The invention provides an automatic software development system based on multi-agent cooperation, and the system comprises a demand analysis agent which is used for receiving a natural language demand input by a shared working area, generating a demand specification document containing an acceptance standard based on the natural language demand, and sending the demand specification document to the shared working area; the detailed design agent is used for receiving a demand specification document input by the shared working area, performing system design based on the demand specification document, generating a design specification document and sending the design specification document to the shared working area; the project development agent is used for receiving a design specification document input by the shared working area, performing code development based on the design specification document, generating an executable code and sending the executable code to the shared working area; and the shared working area is used for storing and forwarding the natural language demand input by the user and the output of the demand analysis intelligent agent, the detailed design intelligent agent and the project development intelligent agent. The development efficiency can be improved, the labor cost is reduced, and the software quality is improved.

The invention discloses a software development requirement document generation method and device, equipment and a storage medium, and relates to the technical field of document generation, and the method comprises the following steps: obtaining first requirement information collected when target software is developed, and carrying out data preprocessing on the first requirement information to obtain corresponding second requirement information; determining each target entity corresponding to the second demand information, and constructing a target knowledge graph based on each target entity; complementing missing information in the second demand information based on the target knowledge graph by using the target large model to obtain third demand information, and performing logic verification on the third demand information to determine target demand information based on a logic verification result; and determining a corresponding target demand document template based on the software development demand type corresponding to the target software, and generating a target software development demand document corresponding to the target software based on the target demand document template and the target demand information. According to the invention, the generation efficiency and accuracy of the software development requirement document can be improved.

The invention discloses a multi-Agent cooperation AADL model AGREE verification statement automatic generation and verification method. The method comprises the following steps: S1, constructing a domain knowledge base; s2, the Agent is analyzed by the AADL model to extract key information; s3, the demand analysis Agent splits atomic propositions from the natural language demand; s4, the AGREE generator Agent generates an AGREE attachment code; s5, the model fusion Agent automatically embeds the AGREE specification into the target AADL component; s6, the AADL verification Agent performs iterative verification and automatic repair, and a multi-dimensional verification system is constructed; and S7, executing AGREE-based AADL architecture formalized verification, and performing logic attribute verification on the grammar compliance model. According to the invention, innovative fusion of a formalized verification method and an intelligent technology is realized, and the core problem that formalized verification of the AADL architecture is difficult to implement is effectively solved.

Solutions for more efficient and effective predictive code recommendation are disclosed. In one example, a method includes identifying a graph-based code recommendation machine learning model, wherein each inferred edge weight value of the graph-based code recommendation machine learning model is updated based at least in part on each compressed forward-adjusted temporal distance measure for an observed co-occurrence of any observed co-occurrences of a predictive code pair for the inferred edge weight value within one or more temporally-proximate occurrence subsets determined based at least in part on a plurality of training predictive code occurrences; processing the input predictive code using the graph-based code recommendation machine learning model to generate one or more related codes of the plurality of predictive codes for the input predictive code; and performing one or more prediction-based actions based at least in part on the one or more related codes.

The invention relates to the technical field of computer data processing, in particular to an intelligent model system for automatically generating a standard protocol interface, which comprises the following steps: acquiring demand information input by a user through a demand acquisition module and converting the demand information into standardized demand parameters; the protocol knowledge base module outputs a compliance rule set according to the target protocol type, and the interface generation module automatically generates an interface source code, a configuration file and an interface document according to the standardized demand parameters and the compliance rule set. The verification module verifies and optimizes the generated interface, and the self-learning module updates system analysis logic and rule content according to a verification result. The system adopts a natural language processing and structured analysis double-path fusion technology to realize accurate analysis of requirements, realizes intelligent matching of protocol rules through a protocol knowledge graph, completes automatic generation of codes based on a symbol reasoning and neural code generation technology, forms a complete closed loop of requirement analysis, protocol adaptation, code generation and verification optimization, and realizes automatic analysis of the requirements. And the interface development efficiency and quality are effectively improved.