One thing I’ve been thinking about something that sounds simple but is actually a little tricky: what do we do after AI fixes a failed test and it passes again?
There are already some interesting approaches to this. mabl looks at adaptive healing, Testim focuses on smarter locators and maintenance, and Applitools approaches changes from the visual validation side. I don’t think there’s one perfect way to handle test maintenance, it really depends on why the test failed in the first place.
While exploring X360 AI Tech, this made me look at the problem a little differently. Getting a test back to green is useful, but I’m more interested in what happened along the way. Looking at the failure details, previous execution, and the actual flow can help answer a basic question: did we really fix the test, or did we just find another way to make it pass? So, after an AI fix, I’d still want to check a few things: Is the test checking the same thing as before? Does it still match the original requirement? Was the failure actually caused by a UI change? And does the fix continue to work in the next few runs?
I’m starting to feel that the real value of AI self-healing isn’t just fixing tests faster. It’s helping QA spend less time fixing tests blindly and more time deciding whether the fix actually makes sense.
What’s the first thing you would check after an AI-healed test turns green?