AI test case generation is everywhere now. Tools claim they read requirements/user stories and spit out full, structured test cases (positive + negative + edges) in seconds. Some even suggest data sets and assertions automatically.
How good is it really in real projects? Do you trust AI-generated cases enough to run them without heavy editing? Share your wins/fails!
AI Test Case Generation – Magic or Mirage in 2026?
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From my experience, ai test case generation has moved from hype to real value — but it’s not fully hands-off yet. In real projects, it’s excellent for quickly drafting structured test cases (positive, negative, edge scenarios) from clear user stories. I usually trust it as a strong first pass, then review and fine-tune before execution or automation. It saves a lot of time on coverage, but critical business rules still need human judgment. Big wins for speed, occasional misses without refinement.
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From my experience, AI test case generation is a strong accelerator rather than a replacement. It speeds up coverage and structure, but human review is still essential for business rules and edge decisions.
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