AI辅助测试
利用大模型技术对软件测试流程进行智能化增强的测试方法，涵盖测试用例自动生成、缺陷预测、日志智能分析、覆盖率分析等环节，代表软件测试工程师能力模型的第三阶段演进。
Core Facts
AI-native development places AI at the core driving position of the development process, where humans only need to provide high-level intent while system design, code writing, and test execution are all completed autonomously by AI—fundamentally different from AI-assisted development (such as GitHub Copilot, which is human-led).
AI-assisted testing can generate test scenarios, summarize execution traces, analyze screenshots, identify suspicious patterns in logs, and infer failure causes, but diagnoses are only hypotheses until verified by an engineer.
Failure-driven testing refers to the practice of immediately distilling a problematic case into a permanent regression test case whenever an incorrect answer caused by context trimming occurs in production.
Timeline (last 90 days)
AI-assisted testing can generate test scenarios, summarize execution traces, analyze screenshots, identify suspicious patterns in logs, and infer failure causes, but diagnoses are only hypotheses until verified by an engineer.
Failure-driven testing refers to the practice of immediately distilling a problematic case into a permanent regression test case whenever an incorrect answer caused by context trimming occurs in production.