Today everyone is talking about AI and self healing locators. You will find hundreds of blogs and articles praising how these tools fix broken tests automatically and reduce maintenance effort. Most of them paint a very optimistic picture. What you will find far less often is an honest discussion about the challenges, trade offs, and new risks that come with this approach.

Self healing sounds simple on the surface. A locator breaks, the tool finds an alternative, the test passes, and everyone is happy. But testing has never been just about making tests pass. It has always been about confidence, correctness, and understanding what the system is actually doing.

To understand the challenges, it helps to first understand what Healinum really is. Healinum is designed to automatically recover from locator failures by identifying similar elements when the original locator no longer works. Instead of failing immediately, it tries to infer intent based on attributes, structure, or past behavior and continues execution. On paper, this reduces flaky failures and saves time spent fixing locators.

In practice, this is where complexity begins. When a test heals itself, it may no longer be interacting with the exact element it was originally designed to test. The test might pass, but it could be validating the wrong behavior. Over time, this creates a false sense of stability where pipelines look green, but coverage slowly drifts away from the original intent.

One of the biggest problems caused by self healing tools is loss of transparency. When a locator is silently healed, teams may not immediately know what changed. Debugging failures becomes harder because the test behavior is no longer deterministic. For less experienced teams, this can mask real application issues and delay meaningful investigation.

Another challenge is trust. Automation is valuable only when teams trust the results. If tests pass because the tool adjusted itself without clear visibility, confidence in automation decreases. Experienced QA engineers understand that not every failure should be healed. Some failures are signals that the product has changed in ways that require human review, not automatic correction.

This is where experienced QA becomes critical. An experienced tester knows when a healed test is acceptable and when it is dangerous. They review changes, question whether the intent still holds, and decide whether the test needs redesign rather than repair. They bring context about user behavior, risk, and product evolution that no healing algorithm can infer reliably.

Self healing also does not remove the need for good test design. Poorly written tests will still be poor tests, even if they heal themselves. Without clear assertions, stable intent, and thoughtful coverage, self healing simply hides problems instead of solving them.

The positive truth is that self healing tools like Healinum are not bad. They are powerful when used correctly. They reduce noise, save time on low value fixes, and help teams focus on more meaningful quality work. But they are not a replacement for judgment.

The real future of automation is not self healing alone. It is self healing combined with human oversight. When AI handles the repetitive recovery and experienced QA handles intent, risk, and decision making, automation becomes both resilient and trustworthy.

The question teams should be asking is not whether self healing tools work.
It is whether they understand when not to let them work automatically.