tdd

name	tdd
description	Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.

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.

If you need concrete test examples to model yours on, read references/tests.md. When a test requires mocks, read references/mocking.md first — it sets the rules for what is mockable.

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

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:

• Confirm with user what interface changes are needed
• Confirm with user which behaviors to test (prioritize)
• Identify opportunities for deep modules (small interface, deep implementation) — when in doubt about what qualifies, read references/deep-modules.md
• Design interfaces for testability — if an interface feels awkward to test, read references/interface-design.md before committing to it
• List the behaviors to test (not implementation steps)
• Get user approval on the plan

Ask: "What should the public interface look like? Which behaviors are most important to test?"

You can't test everything. Confirm with the user exactly which behaviors matter most. Focus testing effort on critical paths and complex logic, not every possible edge case.

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. Before starting the refactor step, read references/refactoring.md — it defines which refactors are safe while GREEN and which aren't.

• Extract duplication
• Deepen modules (move complexity behind simple interfaces)
• Apply SOLID principles where natural
• Consider what new code reveals about existing code
• Run tests after each refactor step

Never refactor while RED. Get to GREEN first.

The Prove-It Pattern (Bug Fixes)

When a bug is reported, do not start by trying to fix it. Start by writing a test that reproduces it.

Bug report arrives
       │
       ▼
  Write a test that demonstrates the bug
       │
       ▼
  Test FAILS (confirming the bug exists)
       │
       ▼
  Implement the fix
       │
       ▼
  Test PASSES (proving the fix works)
       │
       ▼
  Run full test suite (no regressions)

This is still vertical-slice TDD — one RED (reproduction), one GREEN (fix). The test becomes a permanent regression guard.

Example (adapt syntax to your project's language and test framework):

// Bug: "Completing a task doesn't update the completedAt timestamp"

// RED: Reproduce the bug
test "sets completedAt when task is completed":
    task = taskService.createTask({ title: "Test" })
    completed = taskService.completeTask(task.id)

    assert completed.status == "completed"
    assert completed.completedAt is a Date  // FAILS → bug confirmed

// GREEN: Fix the bug
function completeTask(id):
    return db.tasks.update(id, {
        status: "completed",
        completedAt: now()   // This was missing
    })
// Test PASSES → fix proven, regression guarded

For complex bugs, spawn a subagent to write the reproduction test. This separation ensures the test is written without knowledge of the fix, making it more robust.

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