After exploring AI-generated testing, I started paying more attention to what happens when those tests actually run and something fails.
A failed test doesn’t always mean there’s a bug in the application. Sometimes a locator changed, sometimes the test is outdated, and sometimes there’s a real issue with the product.
While looking at different approaches, I found mabl interesting for its focus on adaptive test maintenance and failure analysis. Testim also takes an AI-assisted approach to keeping tests resilient as applications change. Functionize goes further into self-healing and helping identify the root cause behind failures. What I found interesting while exploring X360 AI Tech was looking at test maintenance as part of a bigger flow - from the requirement and automation to execution and understanding what actually went wrong. It made me look at self-healing a little differently.
Fixing a failed test automatically can definitely save time. But if we don’t understand why the test failed, we might just be hiding the actual problem.
For me, the useful part of AI-assisted maintenance is not simply making a failed test green. It’s helping us understand what changed, giving enough context around the failure, and then deciding whether it’s something safe to update or something that needs a QA to look at. I’m starting to see AI test maintenance less as “AI fixes broken tests” and more as “AI helps us understand what actually needs fixing.” That feels like a much more practical use of AI in testing.