一句话：综述 AI/ML 增强的端到端测试自动化工具，梳理其在持续交付场景下的能力与局限。
软件测试用于评估与验证软件是否仍按预期工作，但过程重复、费力且耗时，因此出现了各类自动化测试工具以提升质量并缩短交付周期。然而在 CI/CD 流水线中，被测系统持续变化，传统自动化工具的效果随之下降，测试用例的维护与更新成为主要负担。该文以综述形式考察 AI 增强的端到端测试自动化工具，分析了人工智能与机器学习在测试活动中的应用潜力：自动检测缺陷与错误、维护既有测试用例、以及比人工更快地生成新的测试用例，并讨论了这些方向在持续集成与持续交付环境下的机遇与挑战。
Software testing is a process of evaluating and verifying whether a software product still works as expected, and it is repetitive, laborious, and time-consuming. To address this problem, automation tools have been developed to automate testing activities and enhance quality and delivery time. However, automation tools become less effective with continuous integration and continuous delivery (CI/CD) pipelines when the system under test is constantly changing. Recent advances in artificial intelligence and machine learning (AI/ML) present the potential for addressing important challenges in test automation. AI/ML can be applied to automate various testing activities such as detecting bugs and errors, maintaining existing test cases, or generating new test cases much faster than humans.