Rethinking Unit Tests for AI Development: From Correctness to Contract Protection

Source: Dev.to

The Paradox of Testing AI-Generated Code

When AI writes your code, traditional unit‑testing assumptions break down.

• In conventional development we write tests first (TDD) because humans make mistakes.
  Tests act as a contract—a specification that the implementation must fulfill.
• AI doesn’t make the same mistakes. AI‑generated code at the class or method level is typically correct.
  When I ran fine‑grained unit tests against AI‑written code, they almost always passed on the first try.

So why bother?
The issue isn’t correctness—it’s change detection.

When AI refactors your codebase, it maintains internal consistency beautifully, but it can silently break contracts at boundaries you didn’t explicitly mark:

• An internal class interface changes.
• A namespace’s public surface shifts.
• The code still compiles and the logic seems sound, yet something downstream breaks.

Git diffs don’t help here. When changes span dozens of files, spotting the contract violation becomes needle‑in‑a‑haystack work.

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Test Classification System

I designed a test classification system to understand which tests actually provide value in AI‑assisted development.

Each test class was tagged with its level, e.g.:

[Trait("Level", "L3")] // namespace boundary test

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Observations After Multiple AI Refactoring Cycles

• L1 and L2 tests disappeared – not deliberately deleted, but they became meaningless. AI rewrote internals, and the tests either:
  • Passed trivially (testing already‑correct code)
  • Required constant updates (chasing implementation changes)
  • Tested code that no longer existed
• L3 and L4 tests survived – they caught real issues: interface changes that rippled beyond their intended scope, behavioral shifts at API boundaries, and contracts that AI “improved” without understanding external dependencies.

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Rethinking Unit Tests for AI Development

Traditional unit testing asks: “Is this code correct?”
AI‑era testing should ask: “Has a contract boundary been violated?”

This isn’t Big‑Bang testing or classic integration testing. It’s boundary testing—explicitly marking and protecting the seams in your architecture where changes should not propagate silently.

Practical Guidelines

1. Tag test levels explicitly – the attribute serves dual purpose: test filtering and AI awareness.
2. Focus on namespace boundaries – internal classes may change freely; their aggregate interface should remain stable.
3. Protect public APIs absolutely – these are your external contracts.
4. Let L1/L2 go – don’t fight to maintain tests that provide no signal.
5. Leverage tags – when AI encounters an L3/L4 test, the tag itself communicates: “This boundary matters. Changes here require verification.”

Where Fine‑Grained Tests Still Matter

• Exception handling and edge cases – AI excels at happy paths but can miss subtle error conditions.
• Tests that explicitly exercise exception scenarios, boundary conditions, and failure modes still provide signal—not because AI writes incorrect code, but because these paths may not be exercised during normal AI‑driven development.

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Conclusion

In AI‑assisted development, unit tests transform from correctness verification to change detection. The tests that survive are those that protect contracts at meaningful boundaries—namespace and public API levels.

Stop testing whether AI wrote correct code. Start testing whether AI preserved your contracts.

For implementation examples, see the test structure in Ksql.Linq—an AI‑assisted open‑source project where these patterns evolved through practice.