Automatic test integration system in software development process

Abstract

The invention relates to the technical field of software development and testing, and discloses an automatic testing integrated system in a software development process, which comprises a testing management module used for formulating a testing plan, designing a testing case and managing testing execution; the automatic testing module is used for integrating and executing unit testing, function testing, regression testing, performance testing and safety testing; the continuous integration and continuous delivery module is used for automatically constructing, testing and deploying the process; the environment management module is used for configuring, simulating and virtualizing a test environment; and a reporting and analysis module. Various testing tools such as unit testing, function testing, regression testing, performance testing and safety testing are integrated into one system, so that the problems of diversity and integration difficulty of the testing tools are effectively solved. Different types of testing tools are managed and coordinated in a unified mode, the complexity of the testing process and the compatibility problem between the tools are reduced, and the testing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of software development and testing, and in particular to an automated testing integration system in a software development process. Background Art

[0002] In the modern software development process, testing is a key link to ensure software quality. Existing automated testing tools and systems have several significant problems in practical applications. First, there are many types of existing testing tools, and a single tool can often only cover part of the testing requirements, such as unit testing, functional testing, performance testing, or security testing, which leads to complex testing processes, difficult integration between tools, and increased management and maintenance costs.

[0003] Secondly, the configuration and management of the test environment are important factors in ensuring the accuracy of test results. Different test scenarios and requirements require different test environment configurations. Manual configuration is not only time-consuming and labor-intensive, but also prone to errors, resulting in unreliable test results. In addition, there are inconveniences in the generation and management of test data. Existing tools have limited capabilities in generating diverse and complex test data, especially in test scenarios that require simulation of real user behavior and abnormal data, which affects test coverage and effectiveness.

[0004] Finally, traditional automated testing tools rely on manual operations in generating test cases, optimizing the test process, and analyzing test results. They are inefficient and prone to missing key issues. The analysis of test results and the management of defects are the last step in the testing process. Traditional tools can only provide simple test result reports, lacking in-depth analysis and intelligent decision support, which leads to inefficiency in testers locating problems and improving test strategies. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an automated test integration system in the software development process, which solves the problems of complex test processes and low test efficiency of the currently used test systems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an automated test integration system in the software development process, including a test management module, which is used to formulate test plans, design test cases, and manage test execution;

[0007] Automated testing module for integrating and executing unit testing, functional testing, regression testing, performance testing, and security testing;

[0008] Continuous integration and continuous delivery modules for automating the build, test, and deployment processes;

[0009] Environment management module for configuring, simulating and virtualizing test environments;

[0010] Reporting and analysis module for generating test reports, managing defects, analyzing and visualizing test data;

[0011] User acceptance testing module, used to collect and manage user feedback and perform acceptance testing;

[0012] AI-driven automated test case generation and optimization module, used to provide automated test case generation and optimization, adaptive test environment configuration, intelligent security vulnerability prediction, multi-mode data-driven testing, intelligent defect analysis and classification, real-time feedback and automated decision support.

[0013] Preferably, the test management module includes:

[0014] Test plan management, which is used to develop test plans and define test scope, objectives, schedules, and resource allocations;

[0015] Test case management, which is used to design, store and manage test cases, including functional testing, performance testing and security testing;

[0016] Test execution management is used to manage the test execution process, assign test tasks, and track test progress.

[0017] Preferably, the automated testing module includes:

[0018] Unit testing integration, supporting multiple unit testing frameworks and automatically executing unit testing;

[0019] Functional testing and regression testing, integrating functional testing and regression testing tools for UI and regression testing;

[0020] Performance testing, integrating performance tools for load and performance testing;

[0021] Security testing: integrated security testing tools for security vulnerability scanning and testing.

[0022] Preferably, the continuous integration and continuous delivery module includes:

[0023] CI / CD pipeline, which provides configurable CI / CD pipelines to automate the build, test, and deployment processes;

[0024] Code quality inspection, integrated inspection tools for code quality analysis and reporting;

[0025] Version control integration, providing integration with version control systems to automatically trigger builds and tests.

[0026] Preferably, the environment management module includes:

[0027] Test environment configuration, used to manage and configure the test environment to ensure consistency with the production environment;

[0028] Environment simulation and virtualization: simulate and virtualize the test environment to support multiple test scenarios.

[0029] Preferably, the reporting and analysis module includes:

[0030] Test report generation: automatically generate detailed test reports, including test coverage, test results, and performance indicators;

[0031] Defect tracking and management, integrating defect tracking system to manage and track defects found in testing;

[0032] Data analysis and visualization: Provides analysis and visualization functions for test data to help identify problems and optimize test strategies.

[0033] Preferably, the user acceptance test module includes:

[0034] User feedback management, used to collect and manage feedback and suggestions from user acceptance testing;

[0035] Test case execution allows users to execute specific test cases to verify whether the system meets business requirements.

[0036] Preferably, the AI-driven automated test case generation and optimization module includes:

[0037] Test case generation based on deep learning uses deep learning models to learn from historical test data and user behavior data to automatically generate test cases, covering more comprehensive and complex scenarios;

[0038] Intelligent test case optimization and deduplication analyzes the execution results of test cases through machine learning algorithms, automatically optimizes the test case set, removes redundant and inefficient test cases, and improves testing efficiency.

[0039] Preferably, the test integration system also includes an adaptive test environment configuration module, including:

[0040] Intelligent environment configuration recommendation: Based on AI analysis of project dependencies and configuration requirements, it automatically recommends the best test environment configuration solution to reduce manual configuration errors.

[0041] Environmental health monitoring and self-repair, integrated intelligent monitoring system, real-time monitoring of the health status of the test environment, automatic detection and repair of environmental problems, to ensure the stability of the test environment.

[0042] Preferably, the test integration system also includes an AI guidance function module, including:

[0043] Intelligent guidance system: During the test process, the AI ​​system analyzes the test data in real time and provides instant feedback and optimization suggestions;

[0044] Automated decision support: The AI ​​system automatically determines the priority of test cases based on historical test data, current project status, and code changes, and guides testers to conduct efficient testing.

[0045] Adaptive learning and improvement: The AI ​​system continuously improves and optimizes the test strategy by continuously learning data from the test process to adapt to the needs of different projects and environments.

[0046] The present invention provides an automated test integration system in the software development process.

[0047] Beneficial effects:

[0048] 1. The present invention integrates multiple testing tools such as unit testing, functional testing, regression testing, performance testing and security testing into one system, which effectively solves the problem of diversity of testing tools and difficulty in integration. It uniformly manages and coordinates different types of testing tools, reduces the complexity of the testing process and the compatibility issues between tools, improves testing efficiency, and reduces management and maintenance costs.

[0049] 2. This invention uses AI technology to automatically generate and optimize test cases, solving the problem of low efficiency of traditional automated testing tools in the test case generation and optimization process. Through deep learning models and machine learning algorithms, it learns from historical test data and user behavior data, automatically generates test cases covering comprehensive and complex scenarios, optimizes test case sets, and removes redundant and inefficient test cases.

[0050] 3. The present invention solves the problem of complex test environment configuration and management by adaptively configuring the test environment, automatically recommending and configuring the test environment. The intelligent monitoring system is used to monitor the health status of the test environment in real time, automatically detect and repair environmental problems, and ensure the stability and reliability of the test environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the overall framework of the present invention;

[0052] Figure 2 A schematic diagram of the execution steps of the test management module in the present invention;

[0053] Figure 3 It is a structural diagram of the test management module in the present invention;

[0054] Figure 4 It is a structural schematic diagram of the automated testing module in the present invention;

[0055] Figure 5 It is a structural diagram of the continuous integration and continuous delivery module in the present invention;

[0056] Figure 6 It is a structural schematic diagram of the environment management module in the present invention;

[0057] Figure 7 It is a structural schematic diagram of the reporting and analysis module in the present invention;

[0058] Figure 8 It is a structural schematic diagram of the user acceptance test module in the present invention;

[0059] Fig. 9 This is a schematic diagram of the structure of the AI-driven automated test case generation and optimization module in the present invention;

[0060] Fig.10 It is a structural diagram of the adaptive test environment configuration module in the present invention;

[0061] Fig.11 This is a schematic diagram of the structure of the AI ​​guidance function module in the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Example:

[0064] Please see attached Figure 1 The embodiment of the present invention provides an automated test integration system in a software development process, including:

[0065] The test management module is used to develop test plans, design test cases, and manage test execution; the test management module includes:

[0066] Test plan management, which is used to develop test plans and define test scope, objectives, schedules, and resource allocations;

[0067] Test case management, which is used to design, store and manage test cases, including functional testing, performance testing and security testing;

[0068] Please see attached Figure 2 -Attached Figure 3 ,Test execution management is used to manage the test execution process, assign test tasks, and track test progress.

[0069] In the test plan management process, first determine the test scope R and test objectives G by analyzing the software requirements document (SRS). Then, develop a detailed test plan based on the project schedule T and resource allocation RA. The formula is as follows:

[0070] TP=f(R,G,T,RA)

[0071] Among them, TP represents the test plan and f is the comprehensive function.

[0072] The test plan management stage needs to fully consider the project's risk factors, formulate risk response strategies, and allocate appropriate resources to the testing tasks at each stage to ensure the feasibility and controllability of the test plan.

[0073] Please refer to the attached Figure 4 , automated testing module, used to integrate and execute unit testing, functional testing, regression testing, performance testing and security testing; the automated testing module includes:

[0074] Unit testing integration, supporting multiple unit testing frameworks and automatically executing unit testing;

[0075] Functional testing and regression testing, integrating tools such as Selenium and Appi um for UI and regression testing;

[0076] Performance testing, integrating JMeter, Gat ling and other tools for load and performance testing;

[0077] Security testing, integrating OWASPZAP, BurpSuite and other tools for security vulnerability scanning and testing.

[0078] During the automated testing process, test cases are automatically executed through test scripts, and test results are recorded. Key indicators include test coverage and test execution time. By analyzing the test coverage, the completeness of the test can be determined; and the efficiency of automated testing can be evaluated through the test execution time. In the actual testing process, it is necessary to continuously monitor the running status of the test script, and promptly discover and fix problems in the test script to ensure the smooth progress of the testing process. Test coverage C and test execution time E are key indicators. The formula is as follows:

[0079]

[0080] Among them, TC is the number of test cases executed, TC total is the total number of test cases, t i is the execution time of the i-th test case.

[0081] Please refer to the attached Figure 5, Continuous Integration and Continuous Delivery (CI / CD) module, used to automate the build, test, and deployment processes; the Continuous Integration and Continuous Delivery (CI / CD) module includes:

[0082] CI / CD pipeline, which provides configurable CI / CD pipelines to automate the build, test, and deployment processes;

[0083] Code quality check, integrating tools such as SonarQube for code quality analysis and reporting;

[0084] Version control integration supports integration with version control systems such as Git and SVN, automatically triggering builds and tests.

[0085] In the CI / CD process, the version control system monitors code changes and triggers automatic building and testing. The code quality score QS and the build success rate SR are indicators for evaluating the effectiveness of CI / CD. The formula is as follows:

[0086]

[0087]

[0088] Among them, S code is the score of passing the code quality check, S total is the total score, B success is the number of successful builds, B total is the total number of builds.

[0089] In actual applications, the configuration of the CI / CD pipeline needs to be customized according to the specific needs of the project to ensure that each submission of the code can undergo rigorous quality checks and comprehensive testing, and finally be automatically deployed to the test environment or production environment.

[0090] Please refer to the attached Figure 6 , environment management module, used to configure, simulate and virtualize the test environment; the environment management module includes:

[0091] Test environment configuration, used to manage and configure the test environment to ensure consistency with the production environment;

[0092] Environment simulation and virtualization: Use technologies such as Docker and Kubernetes to simulate and virtualize the test environment and support multiple test scenarios.

[0093] During the test environment configuration process, virtualization technology is used to create a test environment that is consistent with the production environment. The environment configuration correctness rate ECR and the number of simulated environments EN are key indicators. In actual operation, the configuration of the test environment should fully consider various hardware and software dependencies, and use containerization technology (such as Docker) and orchestration tools (such as Kubernetes) to manage and allocate environmental resources to improve the flexibility and repeatability of the environment configuration. The formula is as follows:

[0094]

[0095] Among them, E correct is the number of correctly configured environments, E total is the total number of environments, e j is the jth simulation environment.

[0096] Please see attached Figure 7 , reporting and analysis module, used to generate test reports, manage defects, analyze and visualize test data; the reporting and analysis module includes:

[0097] Test report generation: automatically generate detailed test reports, including test coverage, test results, performance indicators, etc.

[0098] Defect tracking and management, integrating JI RA, Bugzilla and other defect tracking systems to manage and track defects found in testing;

[0099] Data analysis and visualization: Provides analysis and visualization functions for test data to help identify problems and optimize test strategies.

[0100] In the reporting and analysis process, comprehensive test data analysis is provided by automatically generating test reports and defect tracking. Defect repair rate FR and report generation time RT are indicators for evaluating report quality. The test report should record the execution of each test case, the defects found and their repair status in detail, and use data visualization tools to intuitively display various indicators in the test process to help testers and management fully understand the quality status of the project. The formula is as follows:

[0101]

[0102] Among them, D fixed is the number of defects fixed, D total is the total number of defects, t k is the time when the kth report is generated.

[0103] Please see attached Figure 8 , User Acceptance Testing (UAT) module, used to collect and manage user feedback and perform acceptance testing; User Acceptance Testing (UAT) module includes:

[0104] User feedback management, used to collect and manage feedback and suggestions from user acceptance testing;

[0105] Test case execution allows users to execute specific test cases to verify whether the system meets business requirements.

[0106] During the user acceptance test, specific test cases are executed through the system and feedback is provided. User satisfaction US and feedback processing time FT are key indicators. The formula is as follows:

[0107]

[0108] Among them, F positive is the number of positive feedback, F total is the total number of feedbacks, t l is the time to process the lth feedback.

[0109] By establishing an efficient feedback collection and processing mechanism, we ensure that user opinions and suggestions can be reflected in the testing and development process in a timely manner, so as to quickly respond to user needs and improve the system's user experience.

[0110] Please refer to the attached Fig. 9 , AI-driven automated test case generation and optimization module, used to provide automated test case generation and optimization, adaptive test environment configuration, intelligent security vulnerability prediction, multi-mode data-driven testing, intelligent defect analysis and classification, real-time feedback and automated decision support. AI-driven automated test case generation and optimization module, including:

[0111] Test case generation based on deep learning uses deep learning models to learn from historical test data and user behavior data to automatically generate test cases, covering more comprehensive and complex scenarios;

[0112] Intelligent test case optimization and deduplication analyzes the execution results of test cases through machine learning algorithms, automatically optimizes the test case set, removes redundant and inefficient test cases, and improves testing efficiency.

[0113] In the process of AI-generated and optimized test cases, the system learns from historical test data through a deep learning model and automatically generates new test cases. The generated test cases will be optimized through machine learning algorithms to remove redundant and inefficient cases, thereby improving test efficiency and coverage. The coverage of AI-generated and optimized test cases is a key indicator. Through continuous optimization, the quality and effectiveness of test cases can be ensured. The coverage of AI-generated and optimized test cases AIC and optimization efficiency OE are key indicators. The formula is as follows:

[0114]

[0115] Among them, TC AI The number of test cases generated for AI, T manual and T AI The times for manual and AI generation respectively.

[0116] Please see attached Fig.10 ,The test system also includes an adaptive test environment configuration module, including:

[0117] Intelligent environment configuration recommendation: Based on AI analysis of project dependencies and configuration requirements, it automatically recommends the best test environment configuration solution to reduce manual configuration errors.

[0118] Environmental health monitoring and self-repair, integrated intelligent monitoring system, real-time monitoring of the health status of the test environment, automatic detection and repair of environmental problems, to ensure the stability of the test environment.

[0119] During the adaptive test environment configuration process, the system uses AI to analyze project dependencies and configuration requirements, automatically recommend the best test environment configuration solution, and reduce manual configuration errors. The intelligent recommendation system and the environment self-repair rate are key indicators. Through the intelligent monitoring system, the health status of the test environment is monitored in real time, and environmental problems are automatically detected and repaired to ensure the stability and reliability of the test environment.

[0120] Please see attached Fig.11 , the test system also includes enhanced safety test function modules, including:

[0121] AI-driven security vulnerability prediction uses machine learning models to analyze code and historical vulnerability data to predict potential security vulnerabilities in advance and provide repair suggestions;

[0122] Real-time security monitoring and protection: During the test process, the system's security status is monitored in real time, potential attacks are automatically detected and blocked, and security is improved.

[0123] Through AI technology, the system predicts security vulnerabilities. The system uses machine learning models to analyze code and historical vulnerability data, predicts potential security vulnerabilities in advance, and provides repair suggestions. The accuracy of security vulnerability prediction is a key indicator. In addition, the system monitors the security status of the system in real time during the test, automatically detects and blocks potential attacks, and improves the overall security of the system.

[0124] The test system also includes a multi-mode data-driven test module, including:

[0125] Big data analysis and simulation testing: using big data technology to analyze massive amounts of user behavior data, simulate real user operations, and generate test data that is closer to actual usage scenarios;

[0126] Dynamic data generation and mutation testing automatically generate diverse test data, conduct mutation testing, discover the performance of the system under abnormal data, and improve the robustness of the system.

[0127] The system uses big data technology to analyze user behavior data and generate test data that simulates real user operations. The efficiency of multi-modal data generation is a key indicator. In addition, the system automatically generates diverse test data, conducts mutation testing, and evaluates the performance of the system under abnormal data, thereby improving the robustness of the system.

[0128] Multimodal data generation efficiency DGE and mutation test discovery rate MDR are key indicators. The formula is as follows:

[0129]

[0130] Among them, TD generated is the number of test data generated, t total is the total generation time, B found is the number of defects found by mutation testing, B total is the total number of defects. The output data integrity DI formula is as follows:

[0131]

[0132] Where N is the number of test data sets, C p is the completeness score of the pth dataset.

[0133] The test system also includes AI guidance function modules, including:

[0134] Intelligent guidance system: During the test process, the AI ​​system analyzes the test data in real time and provides immediate feedback and optimization suggestions, including test case improvements, defect repair suggestions, etc.

[0135] Automated decision support: The AI ​​system automatically determines the priority of test cases based on historical test data, current project status, and code changes, and guides testers to conduct efficient testing.

[0136] Adaptive learning and improvement: The AI ​​system continuously improves and optimizes the test strategy by continuously learning data from the test process to adapt to the needs of different projects and environments.

[0137] The system analyzes test data in real time through the intelligent guidance system, providing instant feedback and optimization suggestions, including test case improvements and defect repair suggestions. Intelligent guidance efficiency and decision support accuracy are key indicators. The system automatically determines the priority of test cases based on historical test data and current project status, and guides testers to conduct efficient testing. At the same time, the system continuously improves and optimizes test strategies through adaptive learning to ensure that the test plan can adapt to the needs of different projects and environments, and improve the overall test effect and efficiency.

[0138] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The automated test integration system in the software development process is characterized by: include, Test management module, used to develop test plans, design test cases, and manage test execution; Automated testing module for integrating and executing unit testing, functional testing, regression testing, performance testing, and security testing; Continuous integration and continuous delivery modules for automating the build, test, and deployment processes; Environment management module for configuring, simulating and virtualizing test environments; Reporting and analysis module for generating test reports, managing defects, analyzing and visualizing test data; User acceptance testing module, used to collect and manage user feedback and perform acceptance testing; AI-driven automated test case generation and optimization module, used to provide automated test case generation and optimization, adaptive test environment configuration, intelligent security vulnerability prediction, multi-mode data-driven testing, intelligent defect analysis and classification, real-time feedback and automated decision support.

2. The automated test integration system in the software development process according to claim 1, characterized in that: The test management module includes: Test plan management, which is used to develop test plans and define test scope, objectives, schedules, and resource allocations; Test case management, which is used to design, store and manage test cases, including functional testing, performance testing and security testing; Test execution management is used to manage the test execution process, assign test tasks, and track test progress.

3. The automated test integration system in the software development process according to claim 1, characterized in that: The automated testing module includes: Unit testing integration, supporting multiple unit testing frameworks and automatically executing unit testing; Functional testing and regression testing, integrating functional testing and regression testing tools for UI and regression testing; Performance testing, integrating performance tools for load and performance testing; Security testing: integrated security testing tools for security vulnerability scanning and testing.

4. The automated test integration system in the software development process according to claim 1, characterized in that: The continuous integration and continuous delivery modules include: CI / CD pipeline, which provides configurable CI / CD pipelines to automate the build, test, and deployment processes; Code quality inspection, integrated inspection tools for code quality analysis and reporting; Version control integration provides integration with version control systems to automatically trigger builds and tests.

5. The automated test integration system in the software development process according to claim 1, characterized in that: The environment management module includes: Test environment configuration, used to manage and configure the test environment to ensure consistency with the production environment; Environment simulation and virtualization: simulate and virtualize the test environment to support multiple test scenarios.

6. The automated test integration system in the software development process according to claim 1, characterized in that: The reporting and analysis modules include: Test report generation: automatically generate detailed test reports, including test coverage, test results, and performance indicators; Defect tracking and management, integrating defect tracking system to manage and track defects found in testing; Data analysis and visualization: Provides analysis and visualization functions for test data to help identify problems and optimize test strategies.

7. The automated test integration system in the software development process according to claim 1, characterized in that: The user acceptance test module includes: User feedback management, used to collect and manage feedback and suggestions from user acceptance testing; Test case execution allows users to execute specific test cases to verify whether the system meets business requirements.

8. The automated test integration system in the software development process according to claim 1, characterized in that: The AI-driven automated test case generation and optimization module includes: Test case generation based on deep learning uses deep learning models to learn from historical test data and user behavior data to automatically generate test cases, covering more comprehensive and complex scenarios; Intelligent test case optimization and deduplication analyzes the execution results of test cases through machine learning algorithms, automatically optimizes the test case set, removes redundant and inefficient test cases, and improves testing efficiency.

9. The automated test integration system in the software development process according to claim 1, characterized in that: The test integration system also includes an adaptive test environment configuration module, including: Intelligent environment configuration recommendation: Based on AI analysis of project dependencies and configuration requirements, it automatically recommends the best test environment configuration solution to reduce manual configuration errors. Environmental health monitoring and self-repair, integrated intelligent monitoring system, real-time monitoring of the health status of the test environment, automatic detection and repair of environmental problems, to ensure the stability of the test environment.

10. The automated test integration system in the software development process according to claim 1, characterized in that: The test integration system also includes AI guidance function modules, including: Intelligent guidance system: During the test process, the AI ​​system analyzes the test data in real time and provides instant feedback and optimization suggestions; Automated decision support: The AI ​​system automatically determines the priority of test cases based on historical test data, current project status, and code changes, and guides testers to conduct efficient testing. Adaptive learning and improvement: The AI ​​system continuously improves and optimizes the test strategy by continuously learning data from the test process to adapt to the needs of different projects and environments.

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