Curious what’s actually useful vs hype.
Wanted to get a real-world temperature check on this. My company in India (B2B SaaS) is being pushed by management to “use AI in our QA process” — classic top-down pressure without much direction.
I’ve personally tried:
- GitHub Copilot for autocompleting test cases (actually useful for boilerplate)
- Cursor for refactoring old Selenium tests (surprisingly decent)
- One of the newer “autonomous testing” tools I won’t name ! complete waste of money
What’s been your experience? I’m specifically interested in whether anyone is using AI for: Test case generation from requirements documents
That seems like the most promising use case to me but I haven’t found a solid workflow yet.
We use an LLM to convert Gherkin specs into Playwright test scaffolding. It’s not magic but it cuts the initial draft time by maybe 60%. A tester still reviews and completes every test.
The key insight for us was: AI is good at structure and boilerplate 🦾 🦾 , humans are still needed for assertions and edge case logic. 💪💪
Given / When / Then → AI scaffold → Human review → Done
That pipeline actually works in practice. 💪
Excellent thoughts.
Awesome idea man!