AI测试
将人工智能技术应用于软件测试流程的实践，包括AI自动生成用例、AI驱动自动化脚本、AI自愈脚本和智能缺陷分析等核心能力
Core Facts
AI testing requires simultaneously fixing the version under test and the evaluation version; only by fixing one and varying the other does a meaningful horizontal comparison become possible.
AI cannot fully replace test engineers because AI cannot deeply understand complex business processes, make risk judgments, or perform compliance verification, and its outputs require secondary validation by professionals.
The author proposes three systems of testing in the AI era: testing with AI, testing alongside AI (AI-led), and testing against AI (AI application testing).
Timeline (last 90 days)
The correctness of AI behavior cannot be detected with ordinary unit tests and requires the use of evaluation sets; after a prompt is changed, a vision model may miss half the books while the program does not crash.
Testing positions cannot serve as a low-cost fallback for front-end career transitions, because AI automated testing can equally write scripts and execute tests, meaning testing positions face the risk of being replaced.
In AI testing, the quality, distribution, and coverage of training data and test data directly determine model performance, and the weight given to data testing is far greater than in traditional testing.
The author proposes three systems of testing in the AI era: testing with AI, testing alongside AI (AI-led), and testing against AI (AI application testing).
AI testing requires simultaneously fixing the version under test and the evaluation version; only by fixing one and varying the other does a meaningful horizontal comparison become possible.
AI生成的测试内容格式不统一、生成位置分散且缺乏版本追踪，导致测试团队管理混乱
All Facts (7)
AI testing requires simultaneously fixing the version under test and the evaluation version; only by fixing one and varying the other does a meaningful horizontal comparison become possible.
80%VerifiedAI cannot fully replace test engineers because AI cannot deeply understand complex business processes, make risk judgments, or perform compliance verification, and its outputs require secondary validation by professionals.
80%VerifiedThe author proposes three systems of testing in the AI era: testing with AI, testing alongside AI (AI-led), and testing against AI (AI application testing).
65%UnverifiedIn AI testing, the quality, distribution, and coverage of training data and test data directly determine model performance, and the weight given to data testing is far greater than in traditional testing.
60%UnverifiedThe correctness of AI behavior cannot be detected with ordinary unit tests and requires the use of evaluation sets; after a prompt is changed, a vision model may miss half the books while the program does not crash.
50%UnverifiedTesting positions cannot serve as a low-cost fallback for front-end career transitions, because AI automated testing can equally write scripts and execute tests, meaning testing positions face the risk of being replaced.
50%UnverifiedAI生成的测试内容格式不统一、生成位置分散且缺乏版本追踪，导致测试团队管理混乱
50%