146 results about "Software quality" patented technology

The disclosure presents a multi-agent AI system utilizing specialized Large Language Models (LLMs) to automate and enhance software development workflows. This system integrates a memory-augmented generative pre-trained transformer (MemGPT) agent for dynamic context management, a Critic Agent for semi-adversarial quality feedback, and other specialized agents for task delegation and execution. The MemGPT agent interacts with an embedding storage to manage extended contextual information, enabling the system to handle complex software projects with enhanced accuracy and efficiency. This innovative approach significantly reduces manual intervention, streamlines the development process, and improves software quality, offering a robust solution to the challenges of modern software development environments.

The invention discloses an intelligent software testing method and system based on automatic script generation, and relates to the technical field of software testing, and the method comprises the following steps: deploying a distributed data acquisition probe, dynamically adjusting sampling frequency, collecting software operation data, and extracting data features based on a deep learning model; based on the data features, establishing a behavior pattern classification system, and mapping behavior patterns to realize hierarchical behavior classification; the method comprises the steps of constructing a test scene, generating test data, executing a test task, monitoring a test execution state in real time, calculating a test efficiency index, evaluating and feeding back a test effect, and dynamically adjusting a feature extraction strategy. According to the method, deep learning and reinforcement learning technologies are utilized, accurate recognition and classification of software behavior modes are achieved, and comprehensiveness and effectiveness of testing are guaranteed through multi-dimensional coverage rate analysis and a closed-loop optimization mechanism; and meanwhile, the automation level and the intelligent degree of software testing are remarkably improved, and a powerful guarantee is provided for improving the software quality.

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.

The invention provides a software system dependency defect tracking method and system based on a graph database, and relates to the technical field of software, and the method comprises the steps: parsing a source code into an abstract syntax tree, and extracting a dependency; constructing a dependency graph data model; establishing a defect mode feature library; performing pattern matching in the graph data model to identify potential defects; calculating a defect influence range and severity; and generating a defect tracking report, including defect priority ranking, complete call chain construction, defect detailed information recording and optimization suggestion generation. According to the method, the dependency relationship defect in a software system can be effectively identified and tracked, and the software quality is improved.

The invention relates to a test method and device, computer equipment and a readable storage medium. The method comprises the steps of obtaining at least one to-be-tested sub-task corresponding to a to-be-tested task; wherein the test cases in different to-be-tested subtasks are mutually decoupled; adding the to-be-tested sub-tasks to a task queue; and executing each to-be-tested sub-task in the task queue in parallel through at least two task execution modules. By adopting the method, the test efficiency is improved, and the publishing efficiency, the software quality and the software reliability of the to-be-published software with different to-be-tested tasks are further improved.

The invention relates to a low-code application automatic test system and method based on machine learning, and the method comprises the following key steps: firstly, receiving a test demand of a low-code application, and obtaining an interface element graph; secondly, processing the interface element graph through an image processing and semantic recognition technology, and extracting an interface model; then, automatically generating and executing a test case according to the interface model, and collecting test result data; and finally, analyzing test result data and generating a detailed test report. The invention further relates to construction and use of a framework recognition network and a text extraction network to accurately recognize the category of interface elements and text meanings, and test data are deeply analyzed through clustering analysis and a decision tree algorithm, so that abnormal modes, error reasons and paths are effectively recognized. The method can be widely applied to the field of software development, in particular to application testing of a low-code platform, and the software quality and the testing efficiency are improved.

The invention relates to the related technical field of digital data processing, in particular to a software operation informatization evaluation method and system based on artificial intelligence, and the method comprises the steps: determining a software quality standard; effective data is obtained in a random evaluation scene, a first evaluation result is obtained through analysis of a database intelligent module, a second evaluation result is obtained through evaluation of software self-adaption and risk confrontation ability, and a target evaluation result is obtained through mean value calculation after verification of the first evaluation result and the second evaluation result. The technical problem that software operation informatization evaluation results are local and one-sided is solved, effective scene operation data are utilized, evaluation deviation caused by limitation of test samples is avoided, software operation data are deeply mined and analyzed, an elastic relaxation coefficient identifier is determined, and the adaptation flexibility of software under changing environments and demand conditions is ensured. And the basic operation performance of the software and the self-optimization and potential threat resistance capabilities in a continuously changing environment are considered, and the technical effects of completeness of an evaluation level and result accuracy are guaranteed.

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 computer testing, in particular to an incremental code coverage rate calculation method which comprises the following steps: executing a test case on a current version code, and collecting code test coverage information; comparing the baseline version code with the current version code to obtain a change code; based on a baseline version code, performing semantic equivalence analysis on the change code to obtain a logic change code, the semantic equivalence analysis being used for filtering change codes of non-logic factors; and calculating an incremental code coverage rate according to the logic change code and the code test coverage information, wherein the incremental code coverage rate comprises a change line coverage rate and a change link coverage rate. According to the multi-dimensional analysis method, coverage rate evaluation is more scientific and comprehensive, developers can better find test blind spots and potential defects, and therefore the software quality is improved.

Techniques described herein include assessing factors that may affect the quality of software under development by a development team or that may be used to evaluate the health of software already deployed in a computing environment. In one example, this disclosure describes a method that includes collecting, by a computing system, historical information about a plurality of prior development projects; correlating, by the computing system, the historical information with each of a plurality of developers that participated in one or more of the plurality of prior development projects; collecting, by the computing system, information about an application developed by a subset of the plurality of developers; generating, by the computing system and based on the information about the application and the correlated historical information, a predicted outcome for the application; and taking an action, by the computing system and based on the predicted outcome, to prevent the predicted outcome.

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 discloses a software complexity measurement method based on an OWA (Ordered Weighted Average) operator, which comprises the following steps: constructing a software quality evaluation model, and inputting a to-be-evaluated software class diagram into the software quality evaluation model for processing to obtain a software quality evaluation result; wherein the software quality evaluation model is related to the distance between the to-be-evaluated software class diagram and the ideal solution, the distance between the to-be-evaluated software class diagram and the negative ideal solution, the weight of the Euclidean distance between the to-be-evaluated software class diagram and the ideal solution, and the weight of the Euclidean distance between the to-be-evaluated software class diagram and the negative ideal solution. The invention constructs a scientific software quality measurement method.

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 present application discloses a software risk assessment method, apparatus, storage medium, program product and equipment, the method comprising: parsing a code file of the software to be assessed to obtain code information, wherein the code information is used to indicate components and library functions corresponding to the code file; extracting features based on the code information to obtain word vector features, and determining a word vector risk score based on the word vector features; based on the code file, performing a risk assessment on the component to obtain a first static feature risk score, and performing a risk assessment on the library function to obtain a second static feature risk score; determining a static feature risk score based on the first static feature risk score and the second static feature risk score; and determining an overall risk score based on the word vector risk score and the static feature risk score, so as to efficiently and accurately identify risks existing in the software and improve software quality and software security.

The embodiment of the invention relates to the technical field of testing, and discloses an automatic generation management method for a test case, which comprises the following steps of: according to an extracted path, a request method, an input parameter and an output field of an application program interface; whether a pre-configured entity relation data model exists or not is judged, if yes, the pre-configured entity relation data model is obtained, and if not, a test database associated with the to-be-tested item code is obtained, and data analysis is conducted on the test database to obtain the entity relation data model; obtaining association information between the data tables based on the entity relational data model; generating a dependency graph and a field rule base; and generating a test case according to the application program interface document, the dependency graph and the field rule base. According to the automatic generation management method for the test case, through technical means of automation, standardization, flexibility, precise matching and the like, the efficiency and accuracy of test work are greatly improved, and powerful support is provided for software quality guarantee.

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 relates to the technical field of software engineering and software quality evaluation, and discloses a software integration evaluation method based on machine learning and static analysis, and the method comprises the following steps: S1, carrying out static analysis on software source codes, and generating a candidate defect list; s2, executing dynamic verification based on the candidate defect list, and collecting runtime data; s3, performing feature fusion on the static analysis result and the dynamic verification data to form a defect evaluation feature vector; s4, inputting the defect evaluation feature vector into a machine learning model, and evaluating the authenticity and severity of the defect; and S5, outputting a defect detection report. According to the method, by combining static analysis and dynamic verification, the defect and the influence range of the defect can be further confirmed, the false alarm rate in static analysis is effectively reduced, meanwhile, the defect of insufficient coverage of a dynamic verification path is overcome, and the comprehensiveness and accuracy of defect detection are improved.

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 belongs to the field of software simulation, and particularly discloses a CAE software construction method, system and equipment, and the method comprises the steps: determining a simulation scene which needs to be supported by the current version of CAE software, so as to determine a function which needs to be supported by the current version of CAE software; determining a plurality of sub-function modules needing to be added in the CAE software according to the functions needing to be supported; constructing at least one sub-function module in the plurality of sub-function modules by adopting a data-oriented software design architecture; after the plurality of sub-function modules are constructed, completing iteration of the CAE software of the current version to obtain CAE software of an updated version; the CAE software of the updated version supports the simulation scene. Through the method and the device, the problems of software complexity and performance caused by coupling of a plurality of functional modules in a CAE software architecture are solved, so that the development efficiency of CAE software is improved, the software quality and the execution performance are improved, and the simulation experience of a user 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 vehicle-mounted software upgrading method and system and a vehicle, and belongs to the technical field of vehicle-mounted software, and the vehicle-mounted software upgrading method comprises the steps that a software package is analyzed to determine ECU slave modules needing to be upgraded so as to accurately screen out modules actually needing to update software; each ECU slave module needing to be upgraded is upgraded, the upgrading state of each ECU slave module is marked in the corresponding state memory, and according to statistical analysis of the upgrading states of a large number of vehicles, modules or links prone to having upgrading problems are found out, so that the upgrading process and the software quality can be optimized in a targeted mode; by considering the dependency relationship, the collaboration and stability of the whole vehicle electronic system can be guaranteed, the situation that all modules are forced to stop running or roll back to the old version due to failure of part of modules is avoided, and it is guaranteed that the system can be in smooth transition in the upgrading process and continuously and stably exert functions.

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 AHP and an entropy evaluation method. The software quality evaluation method integrates various open source code scanning tools. According to the method, through quantification of an ISO 25010 model, a comprehensive evaluation model covering multiple dimensions of maintainability, testability, reliability, reusability, supportability, safety and the like is constructed. By automatically collecting, analyzing and cleaning code quality data and combining AHP and an entropy evaluation method, the weight of each index is determined, and objective and accurate evaluation of software quality is achieved. The method has the advantages of comprehensiveness, quantification, objectivity, operability and the like, the software quality can be effectively improved, and powerful support is provided for software development.