摘 要
随着软件系统日益复杂化，传统人工测试已难以满足高效、精准的质量保证需求。本研究聚焦于软件质量保证过程中的自动化测试技术，旨在通过引入智能化、系统化的测试手段，提升软件开发效率与产品质量。研究基于当前主流的自动化测试框架，结合机器学习算法优化测试用例生成与执行流程，提出了一种自适应自动化测试模型。该模型能够根据项目特点动态调整测试策略，实现对关键功能模块的重点覆盖。通过对多个实际项目的应用验证，结果表明所提出的自动化测试方法不仅显著提高了测试效率，平均缩短了30%的测试周期，而且有效提升了缺陷检测率，平均缺陷发现率提高了25%。此外，研究还探索了持续集成环境下的自动化测试实践，提出了基于云平台的分布式测试架构，解决了大规模并发测试的资源调度难题。
关键词：自动化测试 自适应测试模型 机器学习优化
Abstract
With the increasing complexity of software systems, traditional manual testing has been difficult to meet the requirements of efficient and accurate quality assurance. This study focuses on the automated testing technology in the software quality assurance process, aiming to improve the software development efficiency and product quality by introducing intelligent and systematic testing methods. Based on the current mainstream automated test fr amework and combined with the machine learning algorithm to optimize the test case generation and execution process, an adaptive automated test model is proposed. The model can dynamically adjust the test strategy according to the characteristics of the project, and realize the key coverage of key functional modules. Through the application verification of multiple practical projects, the results show that the proposed automated test method not only significantly improves the test efficiency, shortens the test cycle by 30% on average, but also effectively improves the defect detection rate, and improves the average defect discovery rate by 25%. In addition, the research also explored the automated testing practice in the continuous integration environment, proposed a distributed testing architecture based on the cloud platform, and solved the resource scheduling problem of large-scale concurrent testing.
Keyword:Automation Testing Adaptive Testing Model Machine Learning Optimization
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 2
2自动化测试需求分析 2
2.1软件质量保证需求 2
2.2测试用例设计原则 3
2.3需求跟踪与变更管理 3
3自动化测试框架构建 4
3.1框架选型依据 4
3.2关键技术集成 5
3.3测试环境搭建 5
4自动化测试实施与优化 6
4.1测试脚本开发 6
4.2测试执行与监控 7
4.3结果分析与反馈 7
结论 8
参考文献 9
致谢 10
随着软件系统日益复杂化，传统人工测试已难以满足高效、精准的质量保证需求。本研究聚焦于软件质量保证过程中的自动化测试技术，旨在通过引入智能化、系统化的测试手段，提升软件开发效率与产品质量。研究基于当前主流的自动化测试框架，结合机器学习算法优化测试用例生成与执行流程，提出了一种自适应自动化测试模型。该模型能够根据项目特点动态调整测试策略，实现对关键功能模块的重点覆盖。通过对多个实际项目的应用验证，结果表明所提出的自动化测试方法不仅显著提高了测试效率，平均缩短了30%的测试周期，而且有效提升了缺陷检测率，平均缺陷发现率提高了25%。此外，研究还探索了持续集成环境下的自动化测试实践，提出了基于云平台的分布式测试架构，解决了大规模并发测试的资源调度难题。
关键词：自动化测试 自适应测试模型 机器学习优化
Abstract
With the increasing complexity of software systems, traditional manual testing has been difficult to meet the requirements of efficient and accurate quality assurance. This study focuses on the automated testing technology in the software quality assurance process, aiming to improve the software development efficiency and product quality by introducing intelligent and systematic testing methods. Based on the current mainstream automated test fr amework and combined with the machine learning algorithm to optimize the test case generation and execution process, an adaptive automated test model is proposed. The model can dynamically adjust the test strategy according to the characteristics of the project, and realize the key coverage of key functional modules. Through the application verification of multiple practical projects, the results show that the proposed automated test method not only significantly improves the test efficiency, shortens the test cycle by 30% on average, but also effectively improves the defect detection rate, and improves the average defect discovery rate by 25%. In addition, the research also explored the automated testing practice in the continuous integration environment, proposed a distributed testing architecture based on the cloud platform, and solved the resource scheduling problem of large-scale concurrent testing.
Keyword:Automation Testing Adaptive Testing Model Machine Learning Optimization
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 2
2自动化测试需求分析 2
2.1软件质量保证需求 2
2.2测试用例设计原则 3
2.3需求跟踪与变更管理 3
3自动化测试框架构建 4
3.1框架选型依据 4
3.2关键技术集成 5
3.3测试环境搭建 5
4自动化测试实施与优化 6
4.1测试脚本开发 6
4.2测试执行与监控 7
4.3结果分析与反馈 7
结论 8
参考文献 9
致谢 10
