175 results about "Software requirements" patented technology

The invention discloses a software configuration item test case automatic generation method based on a large language model, and relates to the technical field of software testing. In order to solve the problems that in the prior art, the flexibility and transportability of a system are limited, the system is difficult to adapt to constantly changing software requirements and designs, and manual intervention is still needed to generate and analyze a test sequence. According to the automatic generation method for the software configuration item test case based on the large language model, through intelligent text segmentation, prompt word construction and test case generation, the efficiency and accuracy of test case generation are improved, the pertinence and coverage of the test case are ensured, and the workload of manually compiling the test case is remarkably reduced; a knowledge graph of software configuration items is constructed, so that the understanding of the model on correlation and similarity among texts is enhanced, and the quality of test case generation is improved; and through a real-time feedback and optimization mechanism, the automation level and the test efficiency of software testing are effectively improved.

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 discloses an automatic test method based on natural language processing, which comprises the following steps: S1, generating a test case: performing semantic analysis on a software requirement document or a test requirement by utilizing a natural language processing technology, extracting key information, and combining the extracted test information and a preset test case template to generate a test case; a preliminary test case is automatically generated; the key information comprises a test purpose, an operation step and an expected result; s2, analyzing the test case; s3, generating a test script; s4, executing the test script; and S5, analyzing a test result. According to the automatic test method based on natural language processing, the test requirements described by the natural language can be automatically analyzed, the test case is generated, the test is automatically executed, and the test report is finally generated, so that the software test efficiency is improved, the workload of manually compiling and executing the test case is reduced, and the test efficiency is improved. And the coverage rate and the accuracy of the test are improved, more software defects can be found, and the quality of software products is guaranteed.

The invention provides a software demand analysis method which can be applied to the field of artificial intelligence. The method comprises the steps that demand information of target software is collected in a multi-modal mode, and initial data of m modals are obtained; wherein m is a positive integer greater than 1; based on the initial data of the m modalities, performing multi-modal demand analysis by using a first large language model to obtain information data of the m modalities; performing information fusion on the information data of the m modes to obtain software demand data; and performing demand optimization processing on the software demand data to obtain a software demand document corresponding to the target software. According to the software demand analysis method and device, intelligent demand analysis and automatic and intelligent processing of the target software are achieved, manual intervention is reduced, the processing efficiency is improved, and the invention further provides a software demand analysis device and device, a storage medium and a program product.

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 present invention provides a method and system for intelligent matching of mobile software information, which relates to the field of intelligent matching technology, including extracting feature vectors by deep learning processing of software requirement text, constructing a software requirement knowledge graph and generating a requirement dependency matrix; analyzing historical test environment data based on the matrix, extracting test environment feature vectors by using a graph neural network; inputting the requirement feature vector and the environment feature vector into a bidirectional long short-term memory network to construct an environment matching model; when there are new requirements, extracting features and calculating matching scores, selecting the optimal environment template for deployment, and continuously optimizing the model through online learning. The present invention improves the accuracy of software test environment matching, reduces the cost of environment configuration, and shortens the test cycle.

The invention provides a test case generation method and device based on a large model, and the method comprises the steps: obtaining a test document which comprises at least one of a software demand document and a software design document; processing the test document, and determining at least one test point; determining reference test cases corresponding to the test points from a test case library; respectively filling the test document, each test point and the corresponding reference test case into corresponding positions in a cue word template to generate a target cue word corresponding to each test point; and inputting each target cue word into a first large model to obtain a test case corresponding to each test point. Therefore, the test case corresponding to each test point is generated through the large model based on the test points, the reference test cases corresponding to the test points and the test document, and the efficiency and accuracy of generating the test cases are improved.

The invention discloses a credential application software testing system and method based on artificial intelligence, and relates to the technical field of computer science and software engineering. The invention discloses a credential application software testing system based on artificial intelligence. The system comprises a demand analysis module, a test case generation module, a self-adaptive test execution module, an intelligent analysis module and a continuous integration and feedback module. The demand analysis module is responsible for analyzing a software demand document; the test case generation module generates a test case according to the demand analysis result; the self-adaptive test execution module is used for testing the generated test case by using a dynamic priority adjustment method; the intelligent analysis module analyzes the test result and generates a test report; and the continuous integration and feedback module is used for integrating the whole test system into a process of artificial intelligence software development, so that intelligent feedback and continuous optimization of the test process are realized.

The invention relates to the technical field of AI demand intelligent analysis, and discloses an AI-based software demand intelligent analysis and prediction method and system, and the method comprises the steps: collecting the use data of software when a software product is used; demand feedback information is generated by acquiring user feedback; carrying out demand analysis on the use data and the demand feedback information by utilizing a transfer learning strategy; and gaming a value analysis result and resource occupation to generate a final software demand prediction result. While the demand satisfaction degree is ensured, the resource consumption is minimized, and the development efficiency is improved. By constructing a function value evaluation system, the method can balance the function income and implementation cost, provides scientific and automatic decision basis for enterprises, greatly reduces the software iteration period, and improves the user experience.

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.

The invention discloses a software test case generation method based on a bidirectional long and short memory algorithm, and belongs to the technical field of software testing. The method comprises the steps of obtaining a to-be-processed file; preprocessing the to-be-processed file to obtain preprocessed text data; inputting the preprocessed text data into the trained Bi-LSTM network for feature extraction to obtain text features; and generating a test case according to the text features. According to the method, the software test case is automatically generated by innovatively applying the bidirectional long and short memory network, and deep understanding of software requirement documents, codes and logs is realized through a deep learning algorithm, so that the correlation and accuracy of the test case are improved. According to the method, the test process can be optimized, and it is ensured that the test cases can cover more test scenes including conventional operation and potential abnormal conditions.

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.

The invention provides a software demand document analysis and test case automatic generation method and system based on a large language model, and the method comprises the following steps: obtaining a software demand document, and analyzing the software demand document to obtain analysis information; carrying out blocking on the analysis information to obtain blocking information; generating a corresponding cue word based on the block information and a test case design method specified by a user to call a large language model, and generating a test case corresponding to the block information based on the cue word; summarizing and formatting the test cases and then displaying the test cases; according to the method, the efficiency and accuracy of test case writing are remarkably improved, the test coverage range is expanded, and the method flexibly adapts to demand changes, so that the software test process is optimized.

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 invention relates to the technical field of system backup, in particular to a factory backup method and device for a domestic operating system, and the method comprises the following steps: after automatically deploying a customized operating system, running a UnisPareted.sh script and a UnisInstall.sh script in sequence in an OEM mode so as to perform partition operation and target lightweight operating system installation operation, and generating a FactoryBackup.ini file; the method comprises the following steps: starting a FactoryBackup.ini file, then restarting target factory equipment, reading backup indication information of the FactoryBackup.ini file through a BIOS (Basic Input / Output System), executing factory backup operation, and finally reporting a factory backup result to an MES (Manufacturing Execution System) server. According to the scheme, the automatic station crossing requirement is met, large-scale deployment is achieved, the production efficiency is improved, customized software needs can be recovered when the system is recovered, and the trouble of reinstallation and reconfiguration is avoided.

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.

A software application is synthesised using one or more processors configured to cause a machine learning (ML)-based generator to perform the following operations, by submitting a series of prompts to the ML-based generator: generate a technical design for the software application based on a set of software requirements; generate a software skeleton for the application based on the technical design; and generate program code for the application based on the software skeleton.