AI Can Generate Unit Tests. But Who Reviews Them?

AI can generate unit tests in seconds. But how do you know whether those tests are actually useful?

Most teams still rely on code coverage and pass rates to evaluate their test suites. The problem is that a test can pass, increase coverage, and still provide little or no additional confidence.

We've been seeing examples where AI-generated tests:

Duplicate existing coverage

Depend on system time or GUID generation

Access files, network resources, or environment variables

Use ineffective or unnecessary mocking

Add maintenance cost without improving quality

Today we launched Typemock Test Review, a tool that analyzes tests during execution and identifies duplicate, fragile, ineffective, and high-maintenance tests.

Instead of looking only at source code, it combines runtime behavior, code coverage, dependency analysis, assertions, and mocking patterns to determine whether a test is actually contributing value.

Some of the issues it can detect:

Duplicate tests

Hidden external dependencies

Flaky test risks

Unused or stale fakes

Ineffective mocking

Tests that increase maintenance without increasing confidence

I'm curious how other teams are dealing with the explosion of AI-generated tests.

Are you reviewing AI-generated tests differently from manually written tests? Have you found good ways to measure test quality beyond coverage and pass/fail metrics?