AI Test Generation: What Works, What Fails, and Why Human Judgment Still Matters

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A test automation engineer with three years of experience shares an honest assessment of using AI tools in their Playwright and Flutter testing workflow over six months. AI proved most useful for converting plain-language bug reports into test scaffolding and suggesting stable locators for complex DOMs, cutting repetitive typing by roughly 60 percent. However, AI consistently defaulted to happy-path scenarios, missing edge cases like duplicate webhook handling that only experience-driven thinking would surface. Performance dropped sharply for mobile web testing, where AI conflated desktop solutions with mobile ones, and was even weaker for Flutter due to sparse training data and outdated API suggestions. The engineer concluded that while AI handles boilerplate efficiently, domain knowledge and real-world debugging experience remain irreplaceable for writing tests that actually catch meaningful bugs.

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