While exploring different AI-powered testing tools, I started noticing that generating a large number of test cases doesn’t necessarily mean we’re getting better tests.
For example, Katalon uses AI to help generate tests from requirements, while Tricentis Tosca takes a more model-based approach to creating and maintaining test assets. Applitools is more focused on using AI for visual validation. Each one seems to solve a different part of the testing process.
I found those differences interesting, and while exploring X360 AI Tech, I started looking at AI-generated testing from another angle - how well the requirement, test scenarios, and automation actually connect with each other.
That made me realize that a good AI-generated test isn't necessarily the one with the most steps or the most scenarios. What matters more is whether it covers something that actually needs to be tested.
The same applies to self-healing. A test being fixed automatically and turning green is useful, but only if it’s still checking what it was originally meant to check.
So lately, I’ve been paying less attention to how many tests AI can generate and more attention to whether those tests actually represent the requirement and provide meaningful coverage.
That shift in perspective has been one of the more interesting things I’ve noticed while exploring AI-assisted testing.
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