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 Sometimes a locator changed, sometimes the test is outdated, and sometimes there’s a real issue with the product.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.
