Use case assessment
Is AI test generation worth building?
Genuinely useful, with one trap that turns coverage into a liability.
Real value on coverage and boilerplate. The trap is that generated tests assert current behaviour, which means they can lock in a bug and defend it.
What it usually means
Generating unit or integration tests from existing code or from a specification.
Is it genuinely AI?
Yes. Writing test cases from code is exactly the kind of structured transformation these models do well, and the productivity gain on boilerplate is real.
The caveat that matters
A test generated from code asserts what the code does. Not what it should do.
If the function has a bug, the generated test asserts the bug. Coverage goes up, the suite goes green, and you have now built a mechanism that will actively resist the fix. Someone correcting the behaviour later will see a failing test and assume they broke something.
That is worse than having no test, because it carries the authority of a passing suite.
How to use it without that
Generate from the specification where one exists, not from the implementation. Where it does not, treat generated tests as drafts requiring the same review as handwritten ones — which removes some of the speed advantage and is the honest trade.
And be suspicious of coverage as the metric. Coverage measures what was executed, not what was verified.
What the simpler version looks like
Ask why coverage is low. If the answer is that the code is hard to test, test generation papers over a design problem. Untestable code is usually code with too many responsibilities, and the tests generated for it will be as awkward as the code.
What it costs to run
Inference is modest. The real cost is review time, and the cost of not reviewing is a suite you cannot trust — which is the expensive failure, because it takes months to discover and undermines every test in it.
Where it sits under the EU AI Act
Not an Annex III high-risk use. As with any coding tool, the live question is where your source code goes and what is retained, which is a contractual and security matter rather than a regulatory one.
When it is worth it
Buy it as part of your existing tooling. Genuinely useful for boilerplate, fixtures and filling coverage on stable, well-understood code.
Not for critical paths where correctness matters most, and not as a way to reach a coverage target. Coverage reached that way measures nothing.
Common questions
What is the risk with AI-generated tests?
Tests generated from code assert what the code does rather than what it should do. If the function has a bug, the test asserts the bug — and then actively resists the fix, because whoever corrects the behaviour sees a failing test and assumes they broke something.
How do you use AI test generation safely?
Generate from the specification rather than the implementation where one exists. Where it does not, review generated tests as carefully as handwritten ones, which removes part of the speed advantage and is the honest trade.
Is low test coverage a reason to use AI generation?
Not on its own. Ask why coverage is low. If the code is hard to test, generation papers over a design problem, and the tests will be as awkward as the code they cover.
Scoring 40 AI use cases in a regulated enterprise — the questions that separated real value from a metric that would have been gamed.