TDD with AI: Writing Specs That Generate Tests

AI + TDD: The Powerful Combination

AI coding tools are excellent at generating test code — when given the right specifications. The combination of human-written specifications and AI-generated tests is more powerful than either alone.

Without specs: AI generates tests that cover the happy path but miss edge cases, error conditions, and security-relevant scenarios.

With specs: AI generates comprehensive tests because the specifications tell it exactly what behavior to verify.

How to Prompt for Test Generation

Effective prompts include:

1. The function signature or component interface
2. The expected behavior for each scenario
3. Edge cases and error conditions
4. The testing framework to use

The Spec-to-Test Pipeline

1. Write requirement in clear format
2. Translate to test case descriptions
3. AI generates test code from descriptions
4. Human reviews: Are assertions meaningful? Are edge cases covered?
5. Implement code to pass the tests

Reviewing AI-Generated Tests

AI tests need human review. A checklist:

• Does it test behavior, not implementation?
• Are assertions meaningful (not just "doesn't throw")?
• Are error paths tested, not just happy paths?
• Are edge cases covered (empty input, boundary values, null)?
• If a bug existed, would this test catch it?

What AI Misses (Add These Manually)

• Security edge cases: script injection, SQL injection
• Race conditions
• Business-specific domain rules
• Authorization failures
• Error propagation when dependencies fail

Key Takeaways

• AI generates tests best when given precise specifications — vague prompts produce vague tests
• The pipeline: write requirement → define test cases → AI generates test code → human reviews
• Review checklist: behavior not implementation, meaningful assertions, error paths, edge cases
• AI generates excellent happy-path tests; humans must add edge cases, security checks, and domain-specific scenarios