I've been looking at a few AI testing platforms recently - mabl, Testim, Functionize, Applitools, LambdaTest, and X360 - and one thing I noticed is that they don't all solve the same problem.
Some focus more on AI-assisted test creation and maintenance, while others are more focused on areas like visual testing or cloud-based browser and device testing.
What I found interesting while comparing them is that test generation is really only one part of the problem.
AI can create a test.
But is it actually the right scenario?
AI can self-heal a broken test.
But did it really fix the test, or did it just make the test pass?
AI can analyse a failure.
But is it an application issue, an environment problem, or a problem with the test itself?
While exploring X360, I started looking more closely at how these different parts of testing can work together - from understanding requirements and coverage to automation, maintenance, and failure analysis.
I don't think this is about AI replacing testers. For me, the more useful idea is letting AI handle some of the repetitive work while QA focuses on things that need actual understanding of the application.
The more I explore this space, the more I feel the important question isn't:
“How much can AI automate?”
It's:
“How do we make sure what AI automates is actually the right thing?”
AI can make testing faster, but QA still needs to make sure we're testing the right thing.
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