A test failing doesn’t always mean there’s something wrong with the test.
Sometimes the test is written correctly, the application is behaving as expected, but something around the test has changed. Maybe the test data was already used, the session wasn’t cleared properly, the environment is in a different state, or a network call took longer than expected. That’s where things get confusing.
When we see a red test, it’s easy to start changing the test itself. But if the real problem was the data or environment, we may end up changing a test that was actually doing its job.
I started thinking about this more while exploring AI-assisted testing. In X360 AI Tech, I came across an approach where the failure is looked at together with the execution context instead of treating the failed test as the problem straight away.
Things like logs, screenshots, traces, test steps, previous results, and application behaviour can help give a clearer picture of what actually happened.
I think this becomes even more important when AI is involved.
If AI sees a failed test and immediately changes something to make it pass, we might get a green result without really understanding what caused the failure in the first place.
For me, the useful question isn’t just: How do we fix this failed test?
It’s: What actually caused this test to fail?
Because sometimes the test isn’t the thing that needs fixing.
