140 results about "Software requirements" patented technology

The invention provides an automatic software testing method and system based on artificial intelligence. The method comprises the following steps: analyzing a software demand document by using an NLP technology, converting the software demand document into structured data, extracting a key function point, and identifying a software function point; generating a test case covering all function points through a deep learning model in combination with historical test data training; automatically executing a test operation, analyzing a test result, analyzing a test log by using a machine learning algorithm, identifying and classifying potential defects by using a data mining and pattern recognition technology, and generating a repair suggestion report; and analyzing feedback data in the test process, dynamically adjusting a strategy according to a test result by using a reinforcement learning algorithm, and optimizing a test case and the strategy. According to the invention, intelligent analysis of test data, automatic generation of test cases and automatic execution of a test process are realized; flexible configuration and expansion can be carried out according to the actual condition of software; the test efficiency and quality are improved, and the test cost and manpower consumption are reduced.

The invention relates to an incidence relation determination method and device, computer equipment, a storage medium and a computer program product. Relates to the technical field of software engineering. The method comprises the following steps: constructing a code knowledge graph based on a source code of a to-be-processed project, and constructing a demand knowledge graph based on a demand document of the to-be-processed project; mining historical data of the to-be-processed project, establishing a tracking link between the code knowledge graph and the demand knowledge graph, and generating a unified knowledge graph; receiving a query request of a user, and performing retrieval in the unified knowledge graph based on the query request to obtain structured context information; and obtaining enhanced prompt information based on the structured context information and a preset prompt template, sending the enhanced prompt information to the large language model, and outputting target result information corresponding to the query request based on the large language model. By adopting the method, the efficiency of determining the incidence relation between the software requirement and the source code can be improved.

The invention provides a software demand defect detection method based on a large language model and a satisfiability problem solver, and the method comprises the steps: constructing a domain knowledge base, guiding the large language model to extract key elements in a demand text through combining with a cue word project, and recognizing imperfect and ambiguous defects; and meanwhile, the demand is converted into a logic expression, and logic inconsistency is detected by using an SAT solver. According to the method, incremental updating of the knowledge base is supported, efficient retrieval and comparison are achieved through vectorization and Hash coding technologies, experiments show that the method is remarkably superior to a traditional technology in demand detection in the fields of finance and the like, defects can be accurately positioned, correction suggestions can be provided, and the efficiency and accuracy of demand analysis are effectively improved.

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 relates to a software function point extraction and element identification method and system, equipment and a medium. According to the method, the natural language processing capability of the large language model and the precise classification capability of the text classification model are combined, so that precise extraction and element recognition of function points in the software requirement document can be realized. And by introducing structured text segmentation, an object attribute cleaning strategy, a cue word optimization extraction scheme, a mechanism of secondary supplemental extraction of ILF function points and a correction optimization strategy based on a heuristic rule, the integrity and accuracy of function point extraction and recognition can be remarkably improved, the situation of mistaken extraction or omission is reduced, and the recognition efficiency is improved. And a solid data basis is provided for software scale automatic estimation.

The invention discloses a software requirement specification document automatic generation method based on a large language model, and the method comprises the steps: text preprocessing: carrying out the category labeling of sentences in software requirement customer opinion data, and dividing a data set into a training set and a verification set; training a demand classifier: classifying texts in the application data set, designing and training a BERT-GCN model, and summarizing different categories of information into different dimensions of software demands; a text multi-classification task: inputting a to-be-processed customer opinion data text into the trained demand classifier for demand text classification, and outputting a classified tagged text; and generating a standard requirement document: inputting the classified tagged text into a large language model, designing a corresponding output prompt template, and guiding the large language model to generate a standardized software requirement specification document. The problems that an existing method for generating the software requirement specification document is low in efficiency and prone to making mistakes are solved.

A system includes processor(s) configured to: receive natural language text describing software requirements for software program; analyze natural language text describing software requirements to identify relationships between different software requirements at least in part by: analyzing how data flows between different software requirements; analyzing how different software requirements influence path and decision points to achieve functionality identified by software requirements; and identifying dependencies between different software requirements; establish sequence for different software requirements based on relationships identified between different software requirements; group plurality of different software requirements together into logical group(s) of software requirements based on sequence for different software requirements and relationships between different software requirements; generate test cases based on logical group(s) of software requirements; execute software program using test cases; and analyze results of execution of software program using test cases to identify any defects in software program.

This invention relates to systems and methods for real-time dynamic classification and orchestration of test automation components in distributed DevOps environments. The system features an Auto Identify Automation (AIA) engine that leverages supervised learning, Multi-Modal Artificial Intelligence (AI), and Generative AI technologies. It includes a Smart Scenario Designer interface that allows users to author test scenarios using handwriting and voice inputs, which are processed in real-time by AI-driven handwriting recognition, voice recognition, and Natural Language Processing (NLP). The system dynamically suggests relevant automated components via a smart bubble pane, facilitating rapid scenario creation. The architecture is tool-agnostic and scalable, with a Shared Workbench Engine that supports real-time collaboration and conflict resolution. The system continuously adapts and improves, ensuring that the automation suite remains consistent, up-to-date, and aligned with evolving software requirements, enabling efficient and user-friendly management of complex test automation processes.

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 discloses an embedded system integration test case multiplexing method and system, and the method is characterized in that the multiplexing of a test case is carried out based on the existing software assets: firstly, based on the existing software requirements and the test case, an existing requirement-test case tracking chain is constructed; performing demand change analysis on the new demand activity graph information; performing change influence range analysis based on a change analysis result in combination with a demand-test case tracking chain, and determining a test case reuse and generation scheme; and finally, executing the test case multiplexing and generating scheme. According to the method, the tracking relation between existing requirements and test case assets is established, meanwhile, the execution cost of downstream test tasks caused by requirement change is reduced through multiplexing, and the test efficiency of integration testing is improved.

The invention discloses a multi-agent system for knowledge-driven automatic software architecture design, and belongs to the technical field of computer aided design, and the multi-agent system comprises an analysis agent, a modeling agent, a design agent and an evaluation agent which are constructed based on a large language model, the analysis agent is used for extracting, screening and classifying requirements which influence the software architecture design from a software requirement specification and generating a requirement document; the modeling agent is used for generating a software architecture view according to a demand document and an external knowledge base; the design agent is used for generating a software architecture document according to the demand document and the software architecture view; and the evaluation agent is used for evaluating and analyzing the software architecture according to the demand document, the software architecture view and the software architecture document to generate an evaluation and analysis report. According to the method, through cooperation and interaction between the intelligent agents and prompt of external knowledge, the reliability, practicability and creativity of software architecture design can be effectively enhanced.

The invention provides an engine software FADEC demand-oriented modeling analysis and test case automatic generation method, and belongs to the field of aero-engine control. According to the method, DeepSeek is used for assisting preprocessing at the system demand level, and modeling work based on a formalized table is completed; through multi-level demand model analysis, vulnerabilities existing in the demand are automatically detected; and finally, automatically generating a test case set meeting DO-178C airworthiness requirements by establishing a grammar logic architecture. The method focuses on formalized verification work of an engine software FADEC demand level, by using an executable model, defects appearing in the demand or design process can be effectively found, errors can be eliminated in time, test cases are automatically generated, and therefore rework and iteration cost in the later period of development is greatly reduced.

The invention discloses an intelligent and automatic test case generation method based on a large language model, and relates to the technical field of large language model application, and the method comprises the following steps: collecting a software demand description file and an interface standardization file of a project, carrying out semantic analysis and mapping, generating a logic constraint set, and establishing a path mapping table; inputting the path mapping table into a large language model, generating a test scene and assertion, and executing semantic drift detection; when semantic drift is detected, a drift report is generated, a reverse correction process is executed, a patch prompt is generated, and the generation chain is executed again; and after the semantic distance of the prompt chain is detected to reach a convergence state, outputting a stable test case set. According to the method, the generation structure is controlled through the deterministic prompt sequence, so that the stability and controllability of test scene and assertion generation are realized; and through semantic drift detection and a reverse correction mechanism, real-time correction of semantic offset of the prompt chain is realized, and the accuracy and stability of the test case are improved.

Embodiments of the present disclosure relate to applications, platforms, architecture, etc. for automating software requirement verification. In particular, one or more generative language model (GLM) prompts may be generated based at least on program information that describes a software program and based on requirement information that corresponds to a requirement of the software program. Based on such prompts, the GLM may be able to automatically identify segments of the software program information that relate to the requirement. Further, based on the identified segments and the GLM prompts, the GLM may be able to automatically create (e.g., based on one or more additional prompts) testing architecture that may be used to verify whether the software program satisfies the requirement.

The invention discloses a componentized container construction system, which comprises an external software source module, a container component source module, a development machine module and an execution machine module, and is characterized in that the development machine module reads an application program, analyzes software dependent on the application program, confirms container component information and generates a dependent software list; packaging the application program and the dependent software list into a task closure; the execution machine module reads a dependent software list in the task closure, obtains container component information meeting dependent software requirements, selects a container component to be used, combines the container component and an application program in the task closure into a root file system by using a joint file system, and sends the root file system to the execution machine module; and generating a configuration file when the container runs according to the configuration information in the container component and the task closure, and starting the container instance from the root file system. The method for constructing the container in a modularized mode has the advantages of being small in size, fast in construction, cross-platform and high in sharing.

The invention discloses an aviation intelligent test platform, and relates to the technical field of aviation test. According to the method, a locally deployed domestic large model and a knowledge base are introduced, a large model fine tuning technology is integrated to realize automatic generation from software requirements to test cases, then on the basis of graph or video identification of a multi-modal large model, characteristic graphs of avionics display control are identified through pictures, a test result is obtained, and the test efficiency is improved. Therefore, automatic execution of the whole testing process is achieved, many problems caused by manual testing are avoided, high efficiency is achieved by reducing labor input, the accuracy of a testing result is improved through an image characteristic recognition technology, and testing requirements of different industry fields can be met by building an exclusive knowledge base and a directional fine tuning model.

The invention provides an automobile software demand document collaborative correction method and system, which are used in the field of software development, and the method comprises the following steps: receiving a demand document input by a user, carrying out grammar detection on the demand document through a semantic analysis engine, and integrating a rule engine and a large language model in the semantic analysis engine; and according to a grammar detection result, marking various grammar errors existing in the demand document through marks of different vision, and synchronously triggering AI automatic correction. Through the scheme, the software requirement document writing and checking efficiency can be improved, and the automobile software development cost can be reduced.

A method includes identifying configuration information of a server being deployed, which includes a configuration and software requirements, and the server is managed by a management server through a management controller controlling a host hosting the server. The method includes retrieving a recommended setup of hardware and software for the server based on the configuration information and retrieving, from a deployment tracking module, a recommended deviation. The method includes creating a deployment file based on the recommended setup and deviation, and transmitting the deployment file to the management controller, which uses the deployment file to configure the server. The deployment tracking module monitors firmware, drivers and / or software deployments of servers for problems and software deployments, monitors websites / databases associated with firmware, drivers and / or software deployed on the servers for updates, analyzes information from monitoring the deployments and updates from the websites / databases, and manages recommended deviations from recommended setups.

The present invention discloses an intelligent software test data generation method and system based on publicly available Internet information. The method includes: collecting data from the Internet based on software requirements and test scenarios and preprocessing it; enhancing the data using multi-objective region Bayesian optimization technology; processing time-series data using time enhancement and variational Bayesian methods; reading software development documents and extracting key information; performing semantic understanding and extension based on a hierarchical classification Gaussian extended semantic model; generating preliminary test data that conforms to business logic; applying the test data and collecting results; and dynamically adjusting the generation strategy using a reinforcement learning algorithm. The present invention also provides a corresponding system. The method and system can automatically generate high-quality test data, improving software test efficiency and quality.

The invention relates to the field of software requirement modeling, and discloses a software requirement modeling method and system based on an artificial intelligence large model.The method comprises the following software requirement modeling steps that firstly, functional structure modeling is conducted, and then functional module modeling is conducted; for each last-stage function module obtained after checking and revising the function structure, function module modeling is carried out one by one according to the following steps: step 1, data structure modeling; step 2, system function modeling; 3, modeling a user interface; through the method, natural language processing and knowledge reasoning capabilities of the artificial intelligence large model can be integrated in a software demand modeling process, collaborative modeling of the artificial intelligence large model and people is realized, intelligent modeling is realized, and the efficiency and quality of software demand modeling are remarkably improved.

The invention discloses an intelligent demand analysis and man-hour evaluation method and system, and belongs to the technical field of computer vision and artificial intelligence. Analyzing the multi-modal demand document containing the text, the picture and the table to generate structured demand data; based on the structured demand data and the system module description, performing module-level splitting on the demand to generate a module task set; retrieving an associated system implementation context for each task in the module task set based on a multi-dimensional knowledge base; fusing the structured demand data, the module task set and the system implementation context to generate a fine-grained technical task; based on a historical case index, calculating reference working hours for the fine-grained technical task; and cross-module cooperative correction and personalized correction based on the ability of the task executor are sequentially performed on the reference working hours to obtain final evaluation working hours, so that automatic deep analysis and accurate and personalized working hour evaluation of software requirements can be realized.

The embodiment of the invention provides an automobile software demand formal conversion and test case generation method and system, electronic equipment and a storage medium, seven core field grammar frameworks including an application range, a trigger condition, a test object, an operation to be executed, a time sequence constraint, a demand type and a generation rule are constructed, time sequence constraints clearly quantify time parameters, demand types cover external input and the like, and a rule is generated to define mapping logic of demands and use cases; the method comprises the following steps: constructing a formalized conversion engine based on AI, automatically converting a natural language demand into a core field through text cleaning, domain model element extraction, element-field incidence matrix mapping and grammar and domain suitability verification, and if verification is not passed, generating a repair suggestion by the AI and performing iterative adjustment; and building a field enhancement cue word containing module test logic, safety standards and the like based on the core field, and inputting a large model subjected to automobile electronic corpus fine adjustment to generate a stepwise use case.

The invention discloses a software demand analysis method based on a knowledge graph, which comprises the following steps of: importing a multi-source demand text to generate a locatable demand text fragment; loading a domain demand ontology, an SHACL / OWL rule template, a term library, a synonym list and a unit and dimension specification library; generating a candidate entity set, a candidate relation set and a candidate triple set, and attaching an evidence fragment; mapping from the relation type to the clause cluster is established, and an OGG-TM model is initialized; outputting a confirmation triple set and a revised candidate entity set; synonymous normalization, unit specification and threshold specification, cross-document entity merging and identifier mapping; reasoning and consistency verification are carried out; and generating a version identifier, and executing limited incremental learning. According to the method, the knowledge graph and the OGG-TM model are fused, demand semantic analysis and self-learning closed loop are achieved, and high precision and traceability are achieved.

A method (600) is presented. The method (600) comprising obtaining (610) an engineering plan and a chiplet arrangement hardware model. The method (600) further comprises estimating (630) a microsystem hardware model based on the hardware requirements and a hardware data set describing hardware resources of the chiplet arrangement and simulating (640) the estimated microsystem hardware model by electrical simulations to provide one or more hardware capabilities. The method (600) further comprises obtaining (650) a chiplet arrangement environment model and estimating (660) a microsystem software model using the hardware capabilities and the software requirements. The method (600) further comprises simulating (670) the estimated microsystem software model on the chiplet arrangement environment model to provide one or more software capabilities and compiling (680) the estimated microsystem hardware model, the estimated microsystem software model, the one or more hardware capabilities, the one or more software capabilities to a microsystem model comprising one or more microsystem capabilities.

The invention discloses a construction method and device of an index data chain, a storage medium and an electronic device, and relates to the technical field of smart home, and the construction method of the index data chain comprises the following steps: determining a plurality of hierarchical demands in a software demand management system; configuring a unique identification code for each hierarchical demand in the plurality of hierarchical demands; based on the plurality of unique identification codes, constructing an index data chain among the plurality of hierarchical demands, the index data chain comprising demand nodes respectively corresponding to the plurality of hierarchical demands, embedding a unique identification code of a first hierarchical demand corresponding to a first demand node in a second demand node corresponding to the first demand node, the first demand node and the second demand node are any two adjacent demand nodes in the index data chain. By adopting the technical scheme, the problem that dynamic association among multi-level requirements cannot be realized in the related technology is solved.

The invention relates to an automatic construction method of a software demand database, which comprises the following steps of: S1, constructing a software demand model expressed by an entity registration book, and generating a demand design document; step S2, processing the demand design document in the step S1; and S3, automatically generating database contents based on the demand design document processed in the step S2. Compared with the prior art, the method has the advantages that the software requirements are standardized and structured, the standardization and accuracy of requirement writing are improved, the database content is automatically generated, and the software research and development efficiency is improved.

The invention provides an embedded software automatic testing method and system based on a model, and relates to the technical field of embedded software testing. The method comprises the steps of obtaining a software demand design project, obtaining a software interface design project, constructing a software test model, automatically decomposing the test model, automatically generating test data, automatically generating a test script, automatically performing test execution and automatically generating a test document. The system comprises a software requirement design engineering analysis module, a software interface design engineering analysis module, a software test model generation module, a test model automatic decomposition module, a test data automatic generation module, a test script automatic generation module, a test execution module and a test document generation module. According to the method, demand design, interface design and a software test platform are integrated together, resources are shared, integrated management of software design and software test is achieved, and the efficiency and quality of software test demand analysis are improved.

The invention discloses a visual software modeling method for a computer software view, which relates to the technical field of computer software and comprises the following steps of preparation before modeling, selection of a visual modeling tool according to software requirements, introduction of a code generation modeling tool, modular modeling, subdivision of a software model into various functional modules and complexity evaluation. The method comprises the following steps of: dividing a software model into functional modules, dynamically adjusting the module dependency, evaluating the overall dependency of the functional modules through an overall evaluation algorithm, and verifying the model, thereby improving the evaluation accuracy, optimizing the modular design, reducing the system complexity and improving the maintainability by subdividing the software model into the functional modules and utilizing a complexity evaluation algorithm and an influence coefficient; through the dependency algorithm, the dependency relationship between the functional modules is quantified, the interaction weight is adjusted, the coupling degree is reduced, the performance bottleneck is avoided, the resource utilization efficiency is optimized, the dependency algorithm is adjusted in real time, the excessive coupling is reduced, the system maintenance cost is reduced, and the modeling efficiency is improved.

The application discloses a kind of deviation degree evaluation method and system between software requirement and code development, it is related to software engineering analysis technical field, including: the chapter title in software requirement specification, table and list item content are matched with functional requirement, according to the matching result, to the corresponding text content, extract the subject and action of function and the object and condition of action, and get requirement function point sentence according to set sentence pattern rule assembly;After extracting syntax unit and function call relationship from code generation, it is matched with the word in the preset code feature library, and the code function point sentence is obtained;According to requirement function point sentence and code function point sentence, calculate deviation degree, combined with preset deviation threshold, obtain evaluation result.Through the difference between code and expected requirement target, objectively evaluate code development deviation degree, solve the problems, such as inefficient collaboration, quality out of control and requirement disconnection, existing in large-scale code development.

The application discloses a kind of software evaluation method, device, equipment and storage medium, and specifically discloses: receiving the software requirement document uploaded by user, and the software requirement document is parsed, and target analysis data is extracted;Target analysis data includes function item description, data element type and file reference type;Based on function item description and interface image associated with function item description, a multi-modal fusion analysis technique is used to classify the function items, resulting in a classification result;The function items are checked for duplicates, resulting in a first inspection result, and the data element types and file reference types are checked for form, resulting in a second inspection result;Form checking includes illegal character checking and character duplication checking;According to the classification result, the first inspection result and the second inspection result, a function point analysis table is generated, and a software evaluation report is generated based on the function point analysis table.