Test-Driven Development

Philosophy

Core principle: Tests should verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't.

Good tests are integration-style: they exercise real code paths through public APIs. They describe what the system does, not how it does it. A good test reads like a specification - "user can checkout with valid cart" tells you exactly what capability exists. These tests survive refactors because they don't care about internal structure.

Bad tests are coupled to implementation. They mock internal collaborators, test private methods, or verify through external means (like querying a database directly instead of using the interface). The warning sign: your test breaks when you refactor, but behavior hasn't changed. If you rename an internal function and tests fail, those tests were testing implementation, not behavior.

See tests.md for examples and mocking.md for mocking guidelines.

Anti-Pattern: Horizontal Slices

DO NOT write all tests first, then all implementation. This is "horizontal slicing" - treating RED as "write all tests" and GREEN as "write all code."

This produces crap tests:

• Tests written in bulk test imagined behavior, not actual behavior
• You end up testing the shape of things (data structures, function signatures) rather than user-facing behavior
• Tests become insensitive to real changes - they pass when behavior breaks, fail when behavior is fine
• You outrun your headlights, committing to test structure before understanding the implementation

LLM-specific failure modes to guard against:

• NEVER rewrite a failing test to make it pass. If a test fails, fix the implementation, not the test. When context runs low, the temptation is to "fix" the test — this defeats the entire purpose of TDD.
• NEVER verify mocks instead of real code paths. Tests must exercise actual behavior.
• Bad tests are debt, not just review problems. Every test created must be maintained forever. Tests not tied to actual behavior become expensive liabilities.

Correct approach: Vertical slices via tracer bullets. One test → one implementation → repeat. Each test responds to what you learned from the previous cycle. Because you just wrote the code, you know exactly what behavior matters and how to verify it.

WRONG (horizontal):
  RED:   test1, test2, test3, test4, test5
  GREEN: impl1, impl2, impl3, impl4, impl5

RIGHT (vertical):
  RED→GREEN: test1→impl1
  RED→GREEN: test2→impl2
  RED→GREEN: test3→impl3
  ...

Workflow

1. Planning

Before writing any code, answer these questions with the user:

1. What interface changes are needed? What functions, methods, or APIs are being added or modified?
2. Which behaviors matter most? You can't test everything. Prioritize critical paths and complex logic over edge cases.
3. Can we design for deep modules? (From "A Philosophy of Software Design") A deep module has a small interface but handles complex logic internally — fewer methods, simpler parameters, more hidden complexity. This makes testing simpler and the API cleaner. Avoid shallow modules: large interfaces with many methods that just pass through to other components. Ask: Can I reduce methods? Simplify parameters? Hide more complexity inside?
4. Can we design for testability? Functions should accept dependencies rather than create them. They should return results instead of producing side effects. See interface-design.md.

• List the behaviors to test (not implementation steps)
• Get user approval on the plan

The better the user's answers to these questions, the higher the code quality.

2. Tracer Bullet

Write ONE test that confirms ONE thing about the system:

RED:   Write test for first behavior → test fails
GREEN: Write minimal code to pass → test passes

This is your tracer bullet - proves the path works end-to-end.

3. Incremental Loop

For each remaining behavior:

RED:   Write next test → fails
GREEN: Minimal code to pass → passes

Rules:

• One test at a time
• Only enough code to pass current test
• Don't anticipate future tests
• Keep tests focused on observable behavior

4. Refactor

After all tests pass, look for refactor candidates:

• Duplication → Extract function/class
• Long methods → Break into private helpers (keep tests on public interface)
• Shallow modules → Combine or deepen
• Feature envy → Move logic to where data lives
• Primitive obsession → Introduce value objects
• Existing code the new code reveals as problematic
• Run tests after each refactor step

Never refactor while RED. Get to GREEN first.

Checklist Per Cycle

[ ] Test describes behavior, not implementation
[ ] Test uses public interface only
[ ] Test would survive internal refactor
[ ] Code is minimal for this test
[ ] No speculative features added