40 results about "Software quality" patented technology

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 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 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 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 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.

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 a software quality uncertainty modeling method based on a graph neural network and Wasserstein comparative learning, and belongs to the technical field of software quality metrics, and the method comprises the steps: firstly, constructing a UML class graph into a heterogeneous graph containing a plurality of node and edge types; extracting node embedding and generating an instance-level dynamic weight vector by using a pre-trained graph attention network; calculating the difference between the node embedding distribution and the predefined positive and negative ideal distribution by adopting a Sliced Wasserstein distance; and finally calculating to obtain a comprehensive complexity score in combination with the dynamic index. According to the method, the topological structure of the class diagram is effectively utilized, accurate measurement and self-adaptive personalized evaluation of the geometric difference of feature distribution are realized, the robustness under fuzzy and incomplete data is improved, and the uncertainty in quality evaluation is explicitly quantified.

The embodiment of the invention relates to the technical field of software development, and discloses a construction management system, method and equipment of a multi-module software architecture and a medium. According to the embodiment of the invention, each module under the multi-module software architecture can be independently managed to meet the requirement of independently issuing each module; codes of all the modules can be stored through a remote warehouse, and version information, change records, review records, storage links and other related information of the modules are managed on a unified platform; the construction project of the whole code can be deployed on a server, and the server can obtain related information of each module from a unified platform according to development requirements and complete code pulling, compiling construction, change statistics and software release processes according to established rules. According to the construction management system provided by the embodiment of the invention, the construction efficiency of the multi-module architecture software can be improved, the traceability of the software is ensured, and the quality and reliability of the software are improved.

The application provides a system account maintenance and index verification method and device, the method comprises the following steps: receiving a unified account construction interface calling instruction sent by a tester; completing the maintenance of a system account to be tested according to configuration information in the unified account construction interface calling instruction; receiving a unified index verification interface calling instruction sent by the tester; and completing index verification according to an index set configured in the unified index verification interface calling instruction. The application automates the process of system account maintenance and index verification by using a unified account construction interface and a unified index verification interface. The goal of full coverage of account types, accurate verification of business indexes, time saving, improvement of test efficiency and improvement of software quality can be achieved by modifying the configuration information in the account construction interface calling instruction according to test points and setting the index set in the unified index verification interface.

The application provides an aviation airborne software quality evaluation method based on a software whole life cycle, which comprises the following steps: S1, establishing an aviation airborne software development process evaluation model; S2, establishing an aviation airborne software product evaluation model; S3, determining a metric element index selection principle; S4, metric element data collection; S5, aviation airborne software quality evaluation calculation, calculating the index values at all levels according to the steps, and completing the software quality evaluation. The application provides an aviation airborne software quality evaluation method based on a software whole life cycle, which is aimed at the deficiency that metric element data is not easy to obtain when evaluating the aviation airborne software quality, analyzes the existing software quality evaluation metric elements, and proposes engineering implementable and data easy to collect metric element indexes. Moreover, based on the collection of the metric element indexes, the influence factors of aviation airborne software development process evaluation and software product evaluation can be fully integrated, so that the software whole life cycle evaluation is realized.

This disclosure provides a model-driven software generation method, apparatus, electronic device, and storage medium, relating to the field of software development technology. It involves model file acquisition and execution environment cleanup, static detection of model specifications, automatic conversion of the model into source code, static scanning of code specifications, and code integration and compilation. The entire process, including the upload of detection results and the target software artifact to the cloud, terminates the process and generates a defect report if the detection fails. Therefore, it can solve the problems in existing technologies caused by non-standardized model development, non-standardized code generation and detection, lack of automated control over the development process, delayed defect discovery, lack of process sharing and traceability, and low team collaboration efficiency. It achieves the technical effects of fully automated development process, standardized models and code, early defect interception, traceable process, improved software quality and development efficiency, and reduced rework costs.

Embodiments of the present application disclose an application program testing method and device, electronic equipment and storage medium. The method comprises: obtaining a first test duration of a random test mode; according to a graph of a to-be-tested application program, executing a segment corresponding to a control in a page of the to-be-tested application program by the random test mode, recording a first segment and a first page that have been executed, obtaining a test result of the first segment, until the test duration of the random test mode reaches the first test duration; the segment is test content corresponding to the control; according to the graph, the first segment and the first page, determining a second segment that has not been executed and a second page where the second segment is located; according to the second page, executing the second segment by a traversal test mode, and obtaining a test result of the second segment. The embodiments of the present application can improve test efficiency and software quality of the application program.

The invention discloses a software quality adaptive measurement method based on information geometry and reinforcement learning. The method comprises the following steps: acquiring original quality feature data of a software class diagram; extracting low-dimensional manifold coordinates through information geometric mapping and sparse representation; reconstructing an index quantity value according to the manifold coordinates and constructing a composite fuzzy matter element; carrying out nondimensionalization on matter elements to obtain an optimal membership matrix; determining an initial weight based on an entropy weight method; iterative optimization is carried out through a reinforcement learning adaptive feedback mechanism to obtain an optimization weight; and calculating a comprehensive evaluation value by adopting an MARCOS method and outputting a measurement result. According to the method, a breakthrough of software quality measurement from static state to dynamic state self-adaption is realized, the accuracy and practicability of measurement are remarkably improved, and the defects of a traditional method in the aspects of high-dimensional complexity, evaluation fuzziness and static weight limitation are effectively overcome.

The invention discloses a software quality measurement method based on FERMATEAN fuzzy PROMETHEE and quantum particle swarm optimization. The method comprises the following steps: acquiring an original characteristic value of a software class diagram and constructing a composite fuzzy matter element; the fuzzy number is converted into a Fermatean fuzzy number meeting cubic constraint so as to expand an uncertainty representation space; calculating a score function and an optimal membership degree to form a standardized matrix; constructing a fitness function fusing the distinction degree and the balance penalty, and adopting a quantum particle swarm optimization algorithm to adaptively learn an optimal index weight; on the basis of the optimal weight and the optimal membership degree, a net flow value of each object is calculated by using a PROMETHEE method; and calculating a comprehensive evaluation index according to the net flow value and dividing quality grades. According to the method, the limitation of a traditional method in the aspects of fuzzy information description, static empowerment and linear compensation is overcome, and the accuracy, the adaptability and the decision reasonability of software quality measurement are improved.

The embodiment of the invention discloses a software quality detection method and device, electronic equipment and a medium, and relates to the technical field of computers.The specific implementation mode of the method comprises the steps that code data, operation data, historical test defect data and historical operation alarm data of software to be detected are obtained; based on the code data, the operation data, the historical test defect data and the historical operation alarm data, constructing a software knowledge graph reflecting an association relationship between internal entities of the to-be-tested software; and based on the software knowledge graph, performing quality analysis on the to-be-tested software to obtain a quality analysis result, and generating a quality analysis report according to the quality analysis result. The method can effectively reduce the human input and the detection period, improves the accuracy and efficiency of software quality detection by means of the deep correlation analysis capability of the knowledge graph, and meets the application scene requirements of large-scale centralized production and high-frequency iteration in the financial industry.

The invention provides a running method and device of an application program and electronic equipment. The running method comprises the following steps: generating a framework code and a framework list of a framework when an application component runs according to a middleware model description file; the application component runtime framework comprises an application component interface module for inputting service logic information, a security monitoring module, a performance statistics module, a task scheduling module, a communication abstraction module, an application component management module, a program entry module and a signal processing module; and developing and running the application program according to the framework code and the framework list. According to the mode, the frame code of the application component runtime frame can be automatically generated based on the middleware model description file, independent development of developers is not needed, the development workload and development adaptation cost of the developers are reduced, the software consistency and software quality are improved, and the development efficiency is improved. Developers only need to input the business logic information in the application component interface module, and complete isolation of the middleware and the application layer can be realized without docking the middleware.

[Problem] The objective is to realize a technical due diligence support method that enables objective evaluation of the technical capabilities of developers. [Solution] A due diligence support method for evaluating the 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 quality based on the information on the source code, and generates a report evaluating the quality based on the results of the analysis. [Selected Figure] Figure 5

The invention relates to a code tracking method and system.The method comprises the steps that a source code file and a code submission record are obtained, the code submission record comprises code submission information and code change content, and the code change content and the source code file have an incidence relation; constructing a corresponding first syntax tree structure for each source code file, wherein the first syntax tree structure comprises a plurality of syntax sub-trees; splitting the code change contents according to the association relationship, constructing a second syntax tree structure for each code change content, and labeling code submission information; and performing similarity matching on the plurality of syntax sub-trees in the second syntax tree structure and the first syntax tree structure, and merging the second syntax tree structure into the first syntax tree structure according to a matching result. The system is used for executing the corresponding method steps. And by using the constructed code tracking syntax tree, rapid tracing of code defects is realized, the code quality risk in a project is further reduced, and the software quality is improved.

According to the progressive software quality verification method and system, code submission is used as a trigger point, seven layers of progressive check from grammar format, content integrity, technical accuracy, consistency, implementation feasibility, business value to final integrated verification are implemented, and each layer is provided with a quantitative standard and a verification algorithm. And upper layer verification can be started after the lower layer reaches the standard, so that systematic control from technical details to business values is realized. Furthermore, automatic correction, content complementation or optimization suggestions are provided based on rules, knowledge bases or large model capabilities, so that intelligent processing from problem discovery to auxiliary repair is realized, and the efficiency is greatly improved.

The invention provides an intelligent software quality measurement method and system based on deep semantic fusion and dynamic weight, and the method comprises the steps: constructing a composite fuzzy matter element matrix of a software class diagram, and carrying out the dimensionless processing of the composite fuzzy matter element matrix, and obtaining an optimal membership matrix; calculating an initial static weight of the complexity measurement index of the software class diagram; optimizing the initial static weight into an instance-level dynamic weight, and obtaining a self-attention value of each complexity measurement index; introducing the self-attention numerical value into the optimal membership matrix, and constructing an enhanced weighted fuzzy compound element matrix in combination with the instance-level dynamic weight; and on the basis of the enhanced weighted fuzzy complex element matrix, a TOPSIS method is adopted, and a final complexity metric value of the software class diagram is calculated in combination with bidirectional projection closeness. The method can adapt to quality evaluation requirements of different types of software systems, and a high-precision, high-adaptability and interpretable intelligent measurement method is provided for software engineering quality management.

The invention discloses a static code testing method and device and a storage medium, and relates to the technical field of software quality assurance and intelligent testing. The method comprises the following steps: performing structured analysis on a target code to generate an abstract syntax tree and a control flow diagram; the target code, the abstract syntax tree and the control flow graph are input into a large language model, and the large language model extracts semantic features of the target code and outputs a causal dependency graph corresponding to the target code; and determining a defect source node based on the causal dependency graph when judging that the target code has a defect. The causal dependency graph realizes causal dependency relationship analysis of the target code, can directly position a defect source node when a defect occurs, realizes comprehensive detection of the defect, reduces resource consumption, realizes defect positioning before actual operation of the target code, solves the hysteresis problem of defect detection, and improves the detection efficiency. And repeated labor of developers is reduced through a mode of automatically generating repair patches, and the error probability of manual repair is reduced.

The invention discloses a code cloning detection method and device, electronic equipment and a storage medium, and relates to the field of computers. The method comprises the following steps: firstly, acquiring historical code files, constructing a historical code file set, and determining the similarity between code change contents of the historical code file set; according to the similarity, a part of historical code file sets are selected as clone code file sets, and computing power resources used for model training are saved; acquiring code change features corresponding to each historical code file in the cloned code file set; training a code clone detection model based on the marked clone code file set and code change features to obtain higher clone detection precision; and performing clone detection on the currently submitted code by using the trained code clone detection model so as to determine a clone detection result, thereby realizing code clone detection of submission-level granularity, improving software quality and reducing software maintenance cost.

The application discloses a test-oriented software quality evaluation method and device, which is used for rapid, automatic and multiple quantitative evaluation of a quality state of a software or system under test in a software test process. The method and device are used in the software test process, and only test data such as software problem scale can drive the evaluation model to automatically operate, so that the cost of carrying out a software quality evaluation activity in the software test process can be reduced, and the efficiency and feasibility of the software quality evaluation can be improved. The extended comprehensive evaluation set vector used can be customized and adjusted by a user, so that flexibility of the evaluation model is ensured.

This invention discloses an adaptive software quality measurement method based on information geometry and reinforcement learning, comprising: acquiring original quality feature data of software class graphs; extracting low-dimensional manifold coordinates through information geometry mapping and sparse representation; reconstructing index values ​​and constructing composite fuzzy matter elements based on manifold coordinates; dimensionlessly transforming the matter elements to obtain an optimal membership matrix; determining initial weights based on the entropy weight method; iteratively optimizing the weights through a reinforcement learning adaptive feedback mechanism; and calculating the comprehensive evaluation value and outputting the measurement result using the MARCOS method. This invention achieves a breakthrough in software quality measurement from static to dynamic adaptation, significantly improving the accuracy and practicality of the measurement, and effectively overcoming the shortcomings of traditional methods in terms of high-dimensional complexity, evaluation fuzziness, and limitations of static weights.

The application discloses a method for automatically positioning a root cause of a defect, which takes a commit for repairing the defect as input, extracts code change lines from the commit, maps each change line to a node of an abstract syntax tree, wherein a node in the graph represents the content of the changed code, and an edge in the graph represents the relationship between the change lines; after obtaining a heterogeneous graph, the graph is input into a heterogeneous graph attention neural network, and an attention mechanism is used to enable each node to obtain the embedding of the current node based on the adjacent nodes of the meta path of the node; the embedding of all nodes is input into a ranking model to obtain the score of the current node; all node scores are ranked, and the node most likely to cause the defect is output. The application realizes a method for automatically positioning the code change causing the root cause of the defect by using the commit for repairing the defect, can automatically identify and position the root cause of the defect, reduces the time and cost spent by the developer, and improves the maintenance efficiency and software quality.

This invention discloses a method for describing the functionality of code snippets based on tree-structured neural network machine learning. First, a code dataset is selected for preprocessing, and an abstract syntax tree (AST) is extracted from the code snippet. The descriptive text of the code is then preprocessed, and subtrees within the AST are identified. Irregular trees are transformed into binary trees, and the AST is embedded to obtain its feature vector set. Finally, a logical description model of the code snippet is constructed and trained. A code snippet without comments is input to generate a functional description. This method can quickly parse irregular code snippets, improving reuse efficiency, software quality, and reliability. It also reduces resource investment during reuse, lowers development costs, and enables faster development and launch of new products to meet market demands and competitive pressures through low-cost software reuse.