AI For Test Generation: Where It Helps And Where It Lies
AI writes tests fast. It also writes tests that look real but verify the wrong things.
You paste a function into an AI. Thirty seconds later, you have twelve passing tests. Your coverage score goes up. You feel productive.
Then a bug hits production. You look at those twelve tests and realize none of them would have caught it.
The AI tested what your code does, not what your code is supposed to do.
AI is useful, but you must know how to use it.
Where AI wins:
- Generating boilerplate like setup and teardown blocks.
- Writing repetitive factory helpers and data objects.
- Creating many variations of a single good test pattern.
- Handling obvious edge cases like null, empty strings, or zero.
Where AI fails:
- Implementation-based tests: It writes tests that follow the code structure instead of the business logic. If you refactor the code, the tests break even if the result is still correct.
- Shallow edge cases: It finds obvious errors but misses domain-specific bugs. It does not know your timezone quirks, your database constraints, or your specific business rules.
- Brittle mocks: It mocks internal services that should remain real. This makes tests slow to maintain and easy to break during refactors.
How to use AI without creating "test theater":
- Define the contract first. Write one sentence in plain English about what the test must prove. Example: "An expired code must return the original amount."
- Give that sentence to the AI. Let the AI write the code, but you must own the intent.
- Mock at the boundary only. Use real instances for your internal modules. Only mock external APIs or databases.
- Write one domain edge case by hand. AI handles the "obvious" edges. You must handle the "3 AM" edges that actually cause production incidents.
Don't let the AI decide what the test verifies. Use it to type the code, but you provide the logic.
Source: https://dev.to/nazar_boyko/ai-for-test-generation-where-it-helps-and-where-it-lies-jhm