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

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 serve as a contract—a specification that the implementation must fulfill.

But AI doesn't make the same mistakes humans do. 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 Real Problem: Silent Contract Breakage

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 compiles. The logic is sound. But something downstream just broke.

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

The Experiment: Four Levels of Tests

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

Level	Scope	Purpose
L1	Method / Class	Verify unit correctness
L2	Cross-class within namespace	Verify internal collaboration
L3	Namespace boundary	Detect internal contract changes
L4	Public API boundary	Protect external contracts

Each test class was tagged with its level:

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

The Result: Natural Selection

After multiple refactoring cycles with AI, something interesting happened:

L1 and L2 tests disappeared.

Not deliberately deleted—they simply became meaningless. AI rewrote internals, and the tests either:

• Passed trivially (testing correct code)
• Required constant updates (chasing implementation changes)
• Tested code that no longer existed

L3 and L4 tests survived.

These caught real issues: interface changes that rippled beyond their intended scope, behavioral shifts at API boundaries, contracts that AI "improved" without understanding their external dependencies.

Level	Survival	Reason
L1	❌ Extinct	AI writes correct code; no detection value
L2	❌ Extinct	AI maintains internal consistency
L3	✅ Survived	Detects namespace boundary violations
L4	✅ Survived	Protects external API contracts

The Shift: From Correctness to Contract Protection

Traditional unit testing asks: "Is this code correct?"

AI-era testing should ask: "Has a contract boundary been violated?"

This isn't BigBang testing or integration testing in the traditional sense. It's boundary testing—explicitly marking and protecting the seams in your architecture where changes should not propagate silently.

Practical Implementation

The key is making boundaries explicit—both for your test runner and for AI:

1. Tag test levels explicitly — The attribute serves dual purpose: test filtering and AI awareness
2. Focus on namespace boundaries — Internal classes change freely; their aggregate interface should not
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

When AI encounters an L3/L4 test, the tag itself communicates: "This boundary matters. Changes here require verification."

Exception: Explicit Edge Cases

One area where fine-grained tests retain value: 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.

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.

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