89 results about "Software quality" patented technology

The invention particularly relates to a unit test case rapid generation method based on an AI large model. According to the unit test case rapid generation method based on the AI large model, structure information is extracted through a static AST, an execution path is tracked through dynamic instrumentation, and code analysis is achieved; an analysis result is converted into cue words through AI, a multi-scene use case is generated through reasoning, and the multi-scene use case is matched with a language framework; counting row / branch / path coverage, identifying gaps and optimizing loop logic; hot spots and paths are displayed through a visual report, IDE one-key repair and manual correction of a feedback model are supported, intelligent retrieval and reuse of historical cases are realized by relying on FAISS, and the generation accuracy is continuously improved. According to the unit test case rapid generation method based on the AI large model, high-coverage-rate test case generation is achieved through the AI large model, the test case generation efficiency is remarkably improved, the test period is shortened, the software quality is improved, the workload of manually compiling and maintaining the test case is reduced, and the development cost is reduced.

An assessment system can generate a software quality value based on testing results and analysis of a multitude of factors that impact a readiness evaluation. For example, the system generates a software quality score (e.g., an Applause Quality Score “AQS”) that enables development teams to understand the level of quality they are achieving for a given release and build-over-build. In various examples, the system generates a data-driven score to enable development teams or quality assurance teams to make decisions for when a build is ready for release. In further embodiments, the system can integrate user interfaces that present a software quality score in a user dashboard that is linked to version control systems. On review and acceptance of the score, a user can trigger the release of their new code or product.

The invention relates to the technical field of coal mine test equipment, in particular to a one-stop automatic test integration platform and a test method for coal mine application software. The one-stop automatic test integration platform comprises an executable test case construction module, an automatic test execution module and a test result management module. According to the automatic test platform and the test method, the functions of test management, system safety penetration, software automatic test, source code static analysis, system performance test, localization adaptation test and the like are integrated; the problem that in the prior art, it is difficult to comprehensively cover all function combinations and abnormal conditions by manually compiling test cases is solved, and the test omission ratio is remarkably reduced; centralized management and correlation analysis of data in each link in the testing process are realized through the data centralized management module, so that the testing efficiency is improved, and the problem positioning is more accurate and rapid; a standardized testing process is integrally formed, and software quality and compliance are guaranteed through all-around testing means.

The invention relates to the technical field of railway traffic control and software quality detection, in particular to a railway signal safety system function performance testing method which comprises the following steps: S1, knowledge extraction: extracting multivariate entities and semantic relationships thereof from text data related to railway signal safety to form a triple set; s2, constructing and optimizing a graph, constructing a heterogeneous knowledge graph, and optimizing the heterogeneous knowledge graph through a graph neural network and an embedded learning method; s3, constructing an intelligent dialogue mechanism; s4, strategy generation and grade evaluation; s5, task scheduling and execution; s6, test data analysis; and S7, result generation and write-back. According to the method, knowledge graph construction, natural language understanding, man-machine collaborative interaction, automatic test strategy generation and graph self-evolution updating technologies are comprehensively applied, and the verification efficiency and result credibility of the special software for the railway signal safety system in the aspects of function integrity and performance stability are effectively improved.

The invention discloses a software quality evaluation method based on dependency chain quality conduction, relates to the technical field of software quality evaluation, and is used for solving the problem that quality conduction between software is difficult to quantify. According to the method, the dependency chain graph covering the component dependency relationship, the version pedigree and the quality index is constructed, the evidence reliability label and the license list are combined, the target component and the conduction path which do not meet the quality evaluation threshold value are accurately recognized, the semantic mapping of the target component and the business function domain is constructed, and in the path conduction analysis stage, the quality of the target component is analyzed. A constraint propagation and causal checking mechanism is introduced, influence is transmitted layer by layer, a real path and an interference path are automatically identified, a comprehensive quality measurement item and a quality grade label of a target component are generated, when evaluation is not met, an upgrading, replacing, isolating or rollback scheme is automatically constructed, compatibility, time window and test influence of the scheme are evaluated, and a test result is obtained. And a closed-loop repair path and a treatment guide are output, so that the response efficiency and decision-making reasonability of the software quality problem are improved.

The invention relates to the technical field of intelligent software quality evaluation, in particular to an intelligent software quality evaluation method based on a multi-modal large model. Comprising the following steps: S1, analyzing a multi-modal data source of software to obtain static structured representation and dynamic trajectory data; constructing an execution perception graph, and calculating a state conversion vector for the edge of the execution perception graph; s2, inputting the execution perception graph into the graph neural network model to generate a final embedding vector, and screening out a high-risk path according to a comparison result of the accumulated risk score and a preset risk threshold value; s3, calculating the defect accessibility probability of the high-risk path; fusing the accumulated risk score and the defect reachability probability to calculate a defect confidence index; and executing a preset processing flow according to an evaluation result of the defect confidence index. According to the method, a complex defect scene is effectively identified, and the problems of false report and missing report of defects caused by single information source are obviously reduced.

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 provides a software quality evaluation method and device for a power grid dispatching automation system. Belongs to the technical field of power dispatching automation. The method comprises the following steps: acquiring running state data of power grid dispatching automation system software, and extracting a third-level index from the running state data; determining subjective and objective fusion weights of the third-level indexes; aggregating the third-level indexes related to the same quality feature according to the subjective and objective fusion weights of the third-level indexes to obtain corresponding second-level indexes; an index vector composed of the second-level indexes is input into the dynamic fuzzy neural network model, and the first-level indexes of the corresponding subsystems are output to serve as comprehensive quality evaluation scores; and determining the software quality grade of the business subsystem according to the comprehensive quality evaluation score and a preset grade threshold value. The method is suitable for power grid dispatching automation system software quality evaluation oriented to complex operation scenes, and comprehensive evaluation and self-adaptive optimization of dispatching software under multi-dimensional and dynamic conditions can be achieved.

The invention discloses an informatization software project cost evaluation system and method, and the system comprises a data collection module, a data processing module, a cost evaluation module and a result output module, and relates to the technical field of software project management. According to the informatization software project cost evaluation system and method, through cooperative work of the cost evaluation unit and the value evaluation unit of the cost evaluation module, a quantitative evaluation model is constructed based on historical data and market dynamics, accurate measurement and calculation of the whole life cycle cost of a software project are realized, and when the deviation analysis unit finds that the cost is abnormal, the evaluation result is accurate. And the data can be immediately fed back to the data processing module and the cost evaluation module for dynamic adjustment, so that a closed-loop management mechanism of estimation-evaluation-deviation correction is formed, the problem of disordered cost management is fundamentally solved, the cost is reduced, and meanwhile, reasonable allocation of resources and stability of software quality are ensured through matching analysis of the cost and the value.

The invention discloses a software quality evaluation method based on a fuzzy theory. The method comprises the following steps: obtaining a quality factor set according to a Meiger software quality model; determining an evaluation grade set, and dividing the evaluation grade set into five evaluation grades; constructing a membership function by adopting a kernel density estimation method; constructing a judgment matrix through the triangular fuzzy number and calculating a defuzzification weight; and converting the score into a fuzzy synthesis value through fuzzy synthesis operation, and generating a fuzzy comprehensive evaluation score through weighted average and normalization processing. According to the method, the fuzzy theory and the Meicole model are combined innovatively, and uncertainty in subjective and objective evaluation is processed by applying fuzzy mathematics. The objectivity, scientificity and repeatability of an evaluation result are effectively improved through a membership function and a weight set constructed based on historical data of similar software. The method is suitable for quality evaluation and optimization decision making of various complex software systems.

The invention discloses an MOM software quality optimization method and system based on dynamic fuzzy scenario, and the method comprises the steps: collecting heterogeneous data from multiple dimensions of operation monitoring, user interaction and business process tracking, extracting a core feature subset through information measurement after cleaning normalization, and defining an operation scene in combination with the characteristics of system load and the like and core feature expression. A double-layer weight model is constructed and dynamically adjusted, fuzzy mapping of features and preset evaluation levels is established for all scenes to form a judgment matrix, the quality level is determined according to the maximum membership degree after weight and matrix synthesis, software configuration, resource allocation or workflow adjustment instructions are generated based on evaluation results to be issued and executed, and closed-loop optimization is achieved. According to the method, accurate quantitative evaluation and automatic optimization are realized.

The application provides a software quality checking method and related products, wherein the method comprises the following steps: generating a checking report of a first software, the checking report comprising a first problem; and matching the first problem with a plurality of problems in a database, wherein the database comprises the plurality of problems and a confirmation result of each problem. In a case where the first problem matches any one of the plurality of problems, the confirmation result of the first problem is determined as the confirmation result of the matched problem; and in a case where the first problem does not match any one of the plurality of problems, the first problem is presented to a user for confirmation of a software problem, so as to determine whether the first problem is a problem in the first software. The method can reduce the workload of the user.

The invention relates to the technical field of electronic design, in particular to a software testing method, computer equipment, a readable storage medium and a program product. According to the software testing method provided by the invention, the testing scheme is generated according to the code of the to-be-detected software through the testing strategy module, so that the pertinence is improved, and the resource consumption of the server is reduced. According to the test scheme, the metadata module and the execution module are utilized to create and run the test case, flexible configuration and automation of the test process are achieved, manpower and time cost is reduced, and efficiency and accuracy are improved. Meanwhile, through multi-level test result judgment of the judgment module, public test judgment and a test case exclusive judgment mode are combined, it is ensured that problems are exposed at the fastest speed, and the test efficiency is improved. The modularized testing process enables the method to have high expandability, new requirements can be met, it is ensured that the software quality is continuously and effectively evaluated, and the testing failure risk is reduced.

The invention discloses a software development big data analysis system, which belongs to the technical field of data analysis, and comprises the following steps: comprehensively collecting various data in a software development process, and carrying out standardization processing on the collected various data to obtain development processing data; performing feature extraction on the development processing data to screen out defect associated features related to software defects; based on the development processing data, constructing a software defect prediction model by using a multiple linear regression algorithm, and training and optimizing the model by adjusting parameters of the model; the method comprises the following steps: acquiring corresponding new data in a target software development process, performing processing and feature extraction on the new data, inputting acquired data values corresponding to defect associated features into a software defect prediction model to obtain a prediction result of the number of software defects, and displaying the prediction result in a visual manner. Developers are facilitated to optimize software design and development processes, and software quality and development efficiency are improved.

The application provides an artificial intelligence-based software test case automatic generation system and method, relates to the technical field of software testing, and comprises the following modules: a project management data acquisition module that collects code metadata and project management data; a dynamic risk weight allocation module that inputs the two types of data into a risk prediction model, outputs a risk heat map, and allocates the dynamic risk weight of the input parameters; a basic test suite generation module that generates a basic test suite according to the input parameters and the dynamic risk weight; and a software test algorithm execution module that starts a software test algorithm and iteratively evolves to generate a test case set. The application solves the technical problem that the existing software test case generation mode is difficult to effectively integrate software related data to identify high-risk links, and the generated test cases are insufficient in covering key areas and low in accuracy, achieves the technical effect of accurately identifying high-risk links of software, and the generated test cases are more targeted, thereby improving software testing efficiency and software quality evaluation accuracy.

The invention provides a test case generation method and device and a test method and device.In the test case generation method, a target demand document used for conducting function testing on a to-be-tested terminal is obtained; and performing content improvement on the demand content in the target demand document based on the information database related to the target demand document to obtain an improved target demand document. And generating a test case and a corresponding expected test result based on the perfected target demand document. According to the scheme, the demand content in the target demand document is subjected to content improvement through the information database, and the test case with comprehensive coverage is generated based on the improved target demand document; according to the method, the problem of incomplete test cases caused by incomplete demand document content or insufficient business proficiency during manual design by testers can be solved, the test comprehensiveness of subsequent software tests is ensured, the software quality of published software is ensured, and the use experience of users is improved.

The invention discloses a software function deep analysis method based on large model driving, and relates to the technical field of software engineering.The software function deep analysis method comprises the steps that multi-modal data of target software is collected, and the multi-modal data at least comprises code library data, demand document data, user interaction logs and test case data; and performing cross-modal alignment processing on the acquired multi-modal data, mapping the data from different modals to a unified vector space, and constructing a multi-dimensional tensor sequence for representing a software state. According to the method, a layered virtualization verification environment comprising a system layer, a business logic layer and a data layer is adopted, a safe, isolated and approximate-to-reality execution platform is provided for the automatically generated test case, and real-time monitoring and verification of software function implementation conditions are supported, so that the test influence on a production environment is reduced, and the test efficiency is improved. And the test feedback cycle is accelerated, and the flexibility and the result reliability of the software quality verification process are comprehensively improved.

[Problem] The objective is to realize a technical due diligence support method that makes it easier to understand the results of software analysis. [Solution] A due diligence support method for evaluating the technical capabilities and software quality of a prospective acquisition company of a business operator, which is performed by a management terminal according to one embodiment of the present invention, wherein the control unit of the management terminal refers to information on the source code disclosed by the developer of the prospective acquisition company, performs an analysis to evaluate the technical capabilities and quality based on the information on the source code, and generates a report evaluating the technical capabilities and quality based on the analysis results. [Selected Figure] Figure 8

The invention relates to software quality verification, in particular to a software quality verification system and method for logic alignment of an execution path and a design blueprint, a design logic atlas construction module is used for converting extracted blueprint logic elements into a design logic atlas in a digraph form and providing a standardized design reference for logic alignment; the execution path atlas construction module is used for converting the collected key program execution track data into an execution path atlas consistent with the design logic atlas in structure; the logic alignment algorithm module is used for calculating map similarity based on a map editing distance, quantitatively evaluating logic consistency between a design logic map and an execution path map, and outputting a logic alignment score; the difference analysis engine is used for carrying out deep analysis on parts which are not successfully logically aligned, identifying logic difference types and carrying out risk level evaluation; according to the technical scheme provided by the invention, the defect that logic consistency verification is difficult to carry out on the program execution logic and the original design intention in the prior art can be effectively overcome.

The application discloses a software quality evaluation method based on a dependency chain quality conduction, relates to the technical field of software quality evaluation, and is used for solving the problem that quality conduction between software is difficult to quantify; the application constructs a dependency chain graph covering the dependency relationship of a component, a version pedigree and a quality index, combines evidence reliability labels and license lists, accurately identifies target components and conduction paths that do not meet a quality evaluation threshold, and constructs semantic mapping of the target components and a business function domain; in a path conduction analysis stage, a constraint propagation and a causal checking mechanism are introduced, influences are transmitted layer by layer, real paths and interference paths are automatically identified, a comprehensive quality measurement item and a quality level label of the target components are generated, when the evaluation does not meet the requirement, an upgrade, replacement, isolation or rollback scheme is automatically constructed, compatibility, a time window and test influence are evaluated, a closed-loop repair path and disposal guidance are output, and the response efficiency and decision rationality of software quality problems are improved.

The embodiment of the application provides an event execution method and device, electronic equipment and a storage medium, wherein an asynchronous event driving class is injected into a software operation service application program in response to a starting operation of the software operation service application program; then an asynchronous event function queue is created according to the asynchronous event driving class through a first component, wherein the asynchronous event function queue contains an asynchronous execution function; further, an event is triggered according to the asynchronous event driving class through a second component, and the event is executed according to the asynchronous event function queue through the first component. The application of the asynchronous event driving class makes the information and data interaction between modules or components of the software operation service application program not be limited by the original framework, and the asynchronous execution function corresponding to the event can be automatically executed in sequence through the associated component when the event is triggered by a certain component, so that the programming complexity is effectively reduced, and the SaaS software quality and stability are further improved.

The invention relates to the technical field of computers, in particular to a metamorphic relation identification method based on program dependency graph features. The method comprises the following steps: obtaining a control flow graph (CFG) of a target program, and obtaining a control dependency graph according to the control flow graph and a forward dominating tree; obtaining a program dependency graph according to the control dependency graph and the data dependency relationship; according to a program analysis requirement slicing criterion C = (N, V) of a target program, graph traversal is conducted on the program dependency graph, program slicing features are obtained, N represents instructions in the target program, and V represents a variable set; and inputting the program slice features into a target prediction model for identification, and obtaining a prediction result corresponding to the metamorphic relationship of the target program. By the adoption of the method and device, the metamorphic relation obtaining accuracy can be improved, the situation that the metamorphic relation cannot be obtained is reduced, program testing convenience can be improved, and software quality is improved.

The application discloses a MOM software quality optimization method and system based on a dynamic fuzzy scene, which comprises the following steps: collecting heterogeneous data in multiple dimensions from operation monitoring, user interaction and business process tracking; extracting a core feature subset through information measurement after cleaning and normalization; defining an operation scene in combination with system load and other features and core feature performance; constructing a double-layer weight model and dynamically adjusting the model; establishing a fuzzy mapping of features and preset evaluation levels for each scene to form a judgment matrix; determining the quality level according to the maximum membership degree after the weight and the matrix are combined; and generating software configuration, resource allocation or workflow adjustment instructions based on the evaluation results and executing the instructions to realize closed-loop optimization. The method realizes precise quantitative evaluation and automatic optimization.

The application provides a model and code cross-validation analysis method based on UPPAAL, a valuable solution is provided by using UPPAAL and a code analysis tool for a double-source code verification method, including system modeling, model verification, model verification analysis, attribute verification, model-code conversion, code verification, model and code cross-validation and the like, which can ensure comprehensive checking of the system, and can ensure the correctness of the syntax semantics, system attributes and behaviors of the system, overcome the limitations (limited range, false positive, lack of integration) of the prior art, and achieve higher level of software quality. It has the advantages of improving code quality, enhancing system reliability, improving efficiency, accuracy and reducing development cost.

The invention provides a control software joint simulation hardware-in-the-loop test system and method. The system comprises a hardware-in-the-loop test platform, a test project management platform and an automatic test platform. The hardware-in-the-loop test platform comprises an IO interface board card and a real-time simulation machine which are respectively used for connecting a physical controller and operating a controlled object model containing a virtual controller model; the system supports the hybrid deployment of a real object and a virtual controller: the controller of a selected type can be accessed in the form of the real object, and meanwhile, a corresponding virtual model in a real-time simulation machine replaces a non-accessed controller of the same type, so that the controller and the real-time simulation machine interact in real time to form a closed-loop test. According to the method, flexible configuration and reuse of a test environment are realized, a whole-process closed loop from test management, automatic execution to result tracing is completed, the method has software quality quantitative measurement and continuous improvement capabilities, and the test efficiency, coverage depth and quality assurance level of the new energy automobile control software are remarkably improved.

The application discloses a kind of distributed systems, and its fault test method, device and medium, it is related to distributed system technical field, for realizing the fault test of distributed system, for the fault superposition problem that traditional fault test scheme is difficult to solve, provide a kind of distributed system fault test method, client / server architecture is adopted, the independent fault test of each node can be realized, whether the fault cause and fault processing meet the expectation are all in-depth to each node, so as to effectively avoid the problem that the fault between multiple nodes is difficult to judge because of superposition. Based on this, the fault test of distributed system is carried out by the method, can improve test efficiency and accuracy, so as to be favorable to improve the software quality of distributed system, reduce maintenance cost, reduce the additional analysis workload of development.

This invention relates to an automated testing system and method for low-code applications based on machine learning, comprising the following key steps: First, receiving test requirements from the low-code application and obtaining interface element diagrams; second, processing the interface element diagrams using image processing and semantic recognition techniques to extract the interface model; then, automatically generating and executing test cases based on the interface model, and collecting test result data; finally, analyzing the test result data and generating a detailed test report. This invention also involves constructing and using a framework recognition network and a text extraction network to accurately identify the categories and text meanings of interface elements, and using clustering analysis and decision tree algorithms to deeply analyze test data, thereby effectively identifying abnormal patterns, error causes, and paths. This invention can be widely applied in the field of software development, especially for application testing on low-code platforms, improving software quality and testing efficiency.

The invention provides a test case optimization method which comprises the steps that multi-source data for a preset software project is acquired, the multi-source data comprises project demand data, code submission information, historical defect description data and a test case, and the test case is optimized according to the project demand data, the code submission information, the historical defect description data and the test case. The method comprises the steps of constructing an initial software quality knowledge graph of a preset software project, detecting a test case based on the initial software quality knowledge graph to obtain a to-be-optimized test case, optimizing the to-be-optimized test case to obtain an optimized test case, and updating the initial software quality knowledge graph according to the optimized test case. And obtaining the target software quality knowledge graph. The multi-source project data are integrated to form the dynamic knowledge graph, accurate diagnosis and intelligent optimization of the test cases are achieved, the test case set can be synchronously evolved along with the software project through graph iteration updating, the test efficiency is improved, and test resource waste and maintenance cost are reduced.

To provide a method for efficiently executing kernel calculation in a short time.SOLUTION: A method for constructing a software failure probability calculation model is constituted so that development quality and verification quality can be calculated based on a body control standard in association with evaluation items conforming to software quality control standards different from those assumed in a first model with evaluation items of the development quality and the verification quality in the first model for calculating a failure probability of software based on the development quality and the verification quality of the software. A conditional probability distribution indicating the degree of influence of the development quality or the verification quality on a quality level is set for the associated evaluation items.SELECTED DRAWING: Figure 1

The invention discloses an automatic test script management method and system, and the method comprises the steps: obtaining and analyzing a test result of script automatic test, and determining a failure case; determining an abnormal feature of the failed use case, and determining an abnormal type of the failed use case based on the abnormal feature; evaluating the abnormal condition of the failed use case based on the abnormal type and the abnormal feature of the failed use case to obtain an abnormal evaluation value; determining the number of failed use cases, and determining a quality coefficient of the script automation test based on the failed use cases and the abnormal evaluation value; and judging whether the quality coefficient of the script automatic test meets a preset requirement or not, and optimizing the script automatic test and re-testing after judging that the quality coefficient does not meet the preset requirement. According to the method, the quality problem in the script test can be quickly found, the script can be timely optimized and improved, and the script test quality can be continuously improved, so that the test efficiency is improved, the test cost is reduced, the software quality and stability are ensured, the user requirements are met, and the competitiveness is improved.