109 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.

The invention discloses a software test result analysis system which comprises a multi-source test data acquisition module, an intelligent anomaly detection engine, a cross-project association analysis module, a visual decision support terminal and a self-adaptive feedback optimization module. The multi-source test data acquisition module integrates heterogeneous test tool data and performs standardization processing; the intelligent anomaly detection engine realizes dynamic anomaly detection by using an LSTM model and a reinforcement learning algorithm; the cross-project association analysis module completes cross-project data association by means of code tracking, a knowledge graph and the like; the visual decision support terminal visually displays an analysis result in the form of a three-dimensional thermodynamic diagram and the like; and the adaptive feedback optimization module forms closed-loop control and dynamically adjusts a test strategy. The system effectively solves the problems that according to a traditional method, multi-source data cannot be fused, anomaly detection is not sensitive, and visual analysis is lacked, efficient analysis of software test results and optimization of the test process are achieved through cooperative work of all the modules, and the test efficiency and the software quality are remarkably improved.

The invention provides a software code defect detection method and system based on program code feature fusion, and the method comprises the steps: carrying out the grammar and structure inspection of a code through a static analysis tool, and extracting defect features related to control flow chaos, exception handling and resource leakage; running a preset test case through a dynamic analysis tool, capturing an execution path and an abnormal behavior in a runtime state, and obtaining defect features which are not covered by static analysis; if the output result of the decision tree model is that defects exist, grouping comprehensive defect feature vectors by adopting a density-based clustering algorithm, determining the types and distribution ranges of the defects, and selecting the density clustering algorithm to process defect features in non-spherical distribution; and according to a clustering result, generating a defect repairing suggestion, combining code logic and execution path information, and determining a specific position and an influence range of the defect. According to the method, the stealth defect in the code can be effectively identified and positioned, the software quality and reliability are improved, and timely and accurate defect repairing guidance is provided for developers.

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.

A defect in the code of a software program is identified. An initial ranking for the defect in the code of the software program is determined. One or more network websites are crawled to identify information associated with the defect in the code of the software program. The information associated with the defect in the defect in the code of the software program is analyzed. In response to analyzing the information associated with the defect in the code of the software program, a second ranking is created for the defect in the code of the software program. The defects in the code of the software program and the second ranking are generated for display in a graphical user interface. By prioritizing which defects are more critical, the quality of the released software improved. In addition, the released software is more secure because critical defects have been removed.

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 belongs to the technical field of artificial intelligence, and provides an artificial intelligence application development system, computer equipment and a storage medium, and the system comprises a data processing module which collects data from various sources, and carries out the preprocessing cloud storage of the data, and obtains a training data set; the model construction module is used for constructing and initializing an AI model according to image recognition and natural language processing in combination with data characteristics, and training the constructed AI model by using a training data set; the model deployment module converts the trained AI model into AP I or micro-service; the prediction reasoning module receives user input, returns a model prediction result, processes batch prediction requests, and provides a prediction result and confidence; the monitoring feedback module monitors the model performance in real time; the user interaction module provides an interaction interface, so that a user obtains required information and service; according to the method, the development and maintenance efficiency can be improved, the software quality is improved, the user experience is optimized, and innovation and commercial value increase are promoted.

The invention discloses a system upgrading method and device, a medium and equipment, and the method comprises the steps: monitoring a running state, calculating a mirror image component quality score and a complete machine software quality score, and intelligently selecting an upgrading mode: when the complete machine software quality score is lower than a first threshold value, carrying out complete machine package upgrading to comprehensively repair system problems; and when the whole machine software quality score reaches the standard but the individual component quality score is lower than a second threshold value, only the target mirror component is upgraded. According to the method, the whole machine package upgrading or the mirror image component upgrading can be accurately selected according to the actual operation condition of the equipment. By dynamically selecting the upgrading mode, not only can unnecessary whole machine upgrading be avoided, but also local problems can be repaired in time, so that the system efficiency, the stability and the user experience are improved.

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 provides an automatic test method and device and electronic equipment, and relates to the technical field of automatic test.The method comprises the steps that in response to starting of a test command, at least one YAML use case matched with a tested item is searched; converting the YAML file of the at least one YAML use case into an executable use case script; starting a use case execution stage, and executing the use case script according to the test logic in the use case execution stage; and in response to completion of execution of the case script, generating a test report of the tested item. According to the automatic testing method and device and the electronic equipment, the threshold of automatic script writing is greatly reduced, powerful support can be provided for testers, rapid regression testing of various tested item types can be achieved, meanwhile, the testers can more efficiently cope with a rapid iteration application environment, and the testing efficiency is improved. And the software quality is ensured to be continuously and effectively guaranteed.

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.

This application belongs to the field of software simulation, and specifically discloses a method, system, and device for constructing a CAE software. The method includes: determining the simulation scenarios that the current version of the CAE software needs to support to determine the functions it needs to support; determining multiple sub-function modules that need to be added to the CAE software according to the functions to be supported; constructing at least one of the multiple sub-function modules using a data-oriented software design architecture; when all the multiple sub-function modules are constructed, completing the iteration of the current version of the CAE software to obtain an updated version of the CAE software; the updated version of the CAE software supports the above-mentioned simulation scenarios. Through this application, the problems of software complexity and performance caused by the coupling of multiple functional modules in the CAE software architecture are solved, thereby improving the development efficiency of the CAE software, enhancing software quality and execution performance, and improving the simulation experience of users.

[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.

An embodiment of the present invention discloses a software testing system. The system includes: a device under test, a measurement and control device, a monitoring device, and a host computer. The host computer is electrically connected to the device under test and the measurement and control device, respectively. The measurement and control device runs test cases and performs testing on the device under test. The monitoring device is electrically connected to the host computer and is used to collect and monitor Controller Area Network (CAN) bus signals from the device under test and the measurement and control device during testing. The embodiment of the present invention implements automated testing of the vehicle controller software by running a model-based test model on the measurement and control device, running the vehicle controller software on the device under test in stages for separate testing. This improves testing efficiency and enhances software quality.

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.