Guides to AI testing

AI testing is the use of AI to create, run and maintain software tests — most often by turning a plain-English description of a flow into a test that drives a real browser. What the term covers, what it does not, and where it still fails.

Nineteen AI testing tools, sorted by what each one needs from you before it can help — a codebase, a QA practice, a pipeline, a budget, or just a URL. That constraint decides more than any feature list.

Writing automated tests as plain English sentences instead of code. How the two generations of it differ, how to write a description that actually works, and where the approach breaks down.

Self-healing repairs test locators automatically when an interface changes. How it works, why it only solves half the problem, and the failure mode nobody selling it mentions.

How to get a working regression suite when nobody's job is testing: which flows to cover first, how many tests you actually need, when to run them, and who owns the result.

You commissioned software from an agency or contractor. How do you verify it does what you paid for, without the source code, an engineering team, or taking the supplier's word for it?