How do you evaluate AI's performance in your testing workflow?

General Discussions
1

We recently integrated an AI-powered tool to help prioritize test cases, and while it felt faster, I wasn’t sure how to measure if it was actually helping.

That got me thinking: how are you evaluating AI’s performance in your testing workflow? Is it about accuracy, speed, or just wider coverage?

:point_down: Cast your vote (you can choose up to 2), and share how you’re tracking AI’s real impact.

  • By accuracy of results
  • By speed of execution
  • By coverage of test scenarios
  • Haven’t evaluated yet
0 voters

Right now, I mostly go by accuracy , if the AI can spot flaky areas or high-risk modules consistently, that’s a win. Still figuring out how to measure ROI properly though :robot:

Curious how others are benchmarking performance. Tools? Metrics? Or just gut feel?

2

I evaluate AI’s performance mainly by accuracy of results and coverage of test scenarios.

If the AI is flagging the right areas, identifying real issues (not false positives), and helping us cover edge cases we might’ve missed, that’s a strong indicator it’s adding value. Speed is great, but if it’s fast and wrong, it’s not really helpful.