When AI Fixes a Test, How Do We Know It Fixed the Right Thing?
While exploring AI-assisted testing, I started thinking more about what actually happens when a test breaks. Tools like mabl, Testim, and Functionize are approaching self-healing in different ways, and I can see why it’s becoming useful as test suites get bigger. But I keep coming back to one thing: just because a healed test passes again, it doesn’t always mean it was fixed correctly. If a…
Exploring the use of AI in testing, I began pondering the process when a test fails. Companies such as mabl, Testim, and Functionize are experimenting with self-healing mechanisms in various ways, which I find increasingly relevant as test suites expand. However, one aspect keeps nagging me: just because a healed test resumes passing, it doesn't necessarily signify that the issue was resolved accurately.
If an AI locates a problem with a locator and discovers an alternate element that allows the test to pass, we still need to verify whether the new element is the one the test was designed to interact with. While delving into these concepts through X360 AI Tech, I've also contemplated how much contextual information is necessary for a test modification to make logical sense.
For me, it's not merely about discovering an alternative that works, but ensuring the correction remains aligned with the original intent of the test. When that point is unclear, I believe a QA review remains crucial. There's a significant distinction between "the test passed again" and "the test passed again, and we are confident it was fixed correctly." I feel this distinction becomes even more critical as test suites continue to grow in size.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
