test-driven-development

---
name: test-driven-development
description: Drives development with tests. Use when implementing any logic, fixing any bug, or changing any behavior. Use when you need to prove that code works, when a bug report arrives, or when you're about to modify existing functionality.
---

# Test-Driven Development

## Overview

Write a failing test before writing the code that makes it pass. For bug fixes, reproduce the bug with a test before attempting a fix. Tests are proof — "seems right" is not done. A codebase with good tests is an AI agent's superpower; a codebase without tests is a liability.

## When to Use

- Implementing any new logic or behavior
- Fixing any bug (the Prove-It Pattern)
- Modifying existing functionality
- Adding edge case handling
- Any change that could break existing behavior

**When NOT to use:** Pure configuration changes, documentation updates, or static content changes that have no behavioral impact.

## The TDD Cycle

```
    RED                GREEN              REFACTOR
 Write a test    Write minimal code    Clean up the
 that fails  ──→  to make it pass  ──→  implementation  ──→  (repeat)
      │                  │                    │
      ▼                  ▼                    ▼
   Test FAILS        Test PASSES         Tests still PASS
```

### Step 1: RED — Write a Failing Test

Write the test first. It must fail. A test that passes immediately proves nothing.

```typescript
// RED: This test fails because createTask doesn't exist yet
describe('TaskService', () => {
  it('creates a task with title and default status', async () => {
    const task = await taskService.createTask({ title: 'Buy groceries' });

    expect(task.id).toBeDefined();
    expect(task.title).toBe('Buy groceries');
    expect(task.status).toBe('pending');
    expect(task.createdAt).toBeInstanceOf(Date);
  });
});
```

### Step 2: GREEN — Make It Pass

Write the minimum code to make the test pass. Don't over-engineer:

```typescript
// GREEN: Minimal implementation
export async function createTask(input: { title: string }): Promise<Task> {
  const task = {
    id: generateId(),
    title: input.title,
    status: 'pending' as const,
    createdAt: new Date(),
  };
  await db.tasks.insert(task);
  return task;
}
```

### Step 3: REFACTOR — Clean Up

With tests green, improve the code without changing behavior:

- Extract shared logic
- Improve naming
- Remove duplication
- Optimize if necessary

Run tests after every refactor step to confirm nothing broke.

## 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)
```

**Example:**

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

// Step 1: Write the reproduction test (it should FAIL)
it('sets completedAt when task is completed', async () => {
  const task = await taskService.createTask({ title: 'Test' });
  const completed = await taskService.completeTask(task.id);

  expect(completed.status).toBe('completed');
  expect(completed.completedAt).toBeInstanceOf(Date);  // This fails → bug confirmed
});

// Step 2: Fix the bug
export async function completeTask(id: string): Promise<Task> {
  return db.tasks.update(id, {
    status: 'completed',
    completedAt: new Date(),  // This was missing
  });
}

// Step 3: Test passes → bug fixed, regression guarded
```

## The Test Pyramid

Invest testing effort according to the pyramid — most tests should be small and fast, with progressively fewer tests at higher levels:

```
          ╱╲
         ╱  ╲         E2E Tests (~5%)
        ╱    ╲        Full user flows, real browser
       ╱──────╲
      ╱        ╲      Integration Tests (~15%)
     ╱          ╲     Component interactions, API boundaries
    ╱────────────╲
   ╱              ╲   Unit Tests (~80%)
  ╱                ╲  Pure logic, isolated, milliseconds each
 ╱──────────────────╲
```

**The Beyonce Rule:** If you liked it, you should have put a test on it. Infrastructure changes, refactoring, and migrations are not responsible for catching your bugs — your tests are. If a change breaks your code and you didn't have a test for it, that's on you.

### Test Sizes (Resource Model)

Beyond the pyramid levels, classify tests by what resources they consume:

| Size | Constraints | Speed | Example |
|------|------------|-------|---------|
| **Small** | Single process, no I/O, no network, no database | Milliseconds | Pure function tests, data transforms |
| **Medium** | Multi-process OK, localhost only, no external services | Seconds | API tests with test DB, component tests |
| **Large** | Multi-machine OK, external services allowed | Minutes | E2E tests, performance benchmarks, staging integration |

Small tests should make up the vast majority of your suite. They're fast, reliable, and easy to debug when they fail.

### Decision Guide

```
Is it pure logic with no side effects?
  → Unit test (small)

Does it cross a boundary (API, database, file system)?
  → Integration test (medium)

Is it a critical user flow that must work end-to-end?
  → E2E test (large) — limit these to critical paths
```

## Writing Good Tests

### Test State, Not Interactions

Assert on the *outcome* of an operation, not on which methods were called internally. Tests that verify method call sequences break when you refactor, even if the behavior is unchanged.

```typescript
// Good: Tests what the function does (state-based)
it('returns tasks sorted by creation date, newest first', async () => {
  const tasks = await listTasks({ sortBy: 'createdAt', sortOrder: 'desc' });
  expect(tasks[0].createdAt.getTime())
    .toBeGreaterThan(tasks[1].createdAt.getTime());
});

// Bad: Tests how the function works internally (interaction-based)
it('calls db.query with ORDER BY created_at DESC', async () => {
  await listTasks({ sortBy: 'createdAt', sortOrder: 'desc' });
  expect(db.query).toHaveBeenCalledWith(
    expect.stringContaining('ORDER BY created_at DESC')
  );
});
```

### DAMP Over DRY in Tests

In production code, DRY (Don't Repeat Yourself) is usually right. In tests, **DAMP (Descriptive And Meaningful Phrases)** is better. A test should read like a specification — each test should tell a complete story without requiring the reader to trace through shared helpers.

```typescript
// DAMP: Each test is self-contained and readable
it('rejects tasks with empty titles', () => {
  const input = { title: '', assignee: 'user-1' };
  expect(() => createTask(input)).toThrow('Title is required');
});

it('trims whitespace from titles', () => {
  const input = { title: '  Buy groceries  ', assignee: 'user-1' };
  const task = createTask(input);
  expect(task.title).toBe('Buy groceries');
});

// Over-DRY: Shared setup obscures what each test actually verifies
// (Don't do this just to avoid repeating the input shape)
```

Duplication in tests is acceptable when it makes each test independently understandable.

### Prefer Real Implementations Over Mocks

Use the simplest test double that gets the job done. The more your tests use real code, the more confidence they provide.

```
Preference order (most to least preferred):
1. Real implementation  → Highest confidence, catches real bugs
2. Fake                 → In-memory version of a dependency (e.g., fake DB)
3. Stub                 → Returns canned data, no behavior
4. Mock (interaction)   → Verifies method calls — use sparingly
```

**Use mocks only when:** the real implementation is too slow, non-deterministic, or has side effects you can't control (external APIs, email sending). Over-mocking creates tests that pass while production breaks.

### Use the Arrange-Act-Assert Pattern

```typescript
it('marks overdue tasks when deadline has passed', () => {
  // Arrange: Set up the test scenario
  const task = createTask({
    title: 'Test',
    deadline: new Date('2025-01-01'),
  });

  // Act: Perform the action being tested
  const result = checkOverdue(task, new Date('2025-01-02'));

  // Assert: Verify the outcome
  expect(result.isOverdue).toBe(true);
});
```

### One Assertion Per Concept

```typescript
// Good: Each test verifies one behavior
it('rejects empty titles', () => { ... });
it('trims whitespace from titles', () => { ... });
it('enforces maximum title length', () => { ... });

// Bad: Everything in one test
it('validates titles correctly', () => {
  expect(() => createTask({ title: '' })).toThrow();
  expect(createTask({ title: '  hello  ' }).title).toBe('hello');
  expect(() => createTask({ title: 'a'.repeat(256) })).toThrow();
});
```

### Name Tests Descriptively

```typescript
// Good: Reads like a specification
describe('TaskService.completeTask', () => {
  it('sets status to completed and records timestamp', ...);
  it('throws NotFoundError for non-existent task', ...);
  it('is idempotent — completing an already-completed task is a no-op', ...);
  it('sends notification to task assignee', ...);
});

// Bad: Vague names
describe('TaskService', () => {
  it('works', ...);
  it('handles errors', ...);
  it('test 3', ...);
});
```

## Test Anti-Patterns to Avoid

| Anti-Pattern | Problem | Fix |
|---|---|---|
| Testing implementation details | Tests break when refactoring even if behavior is unchanged | Test inputs and outputs, not internal structure |
| Flaky tests (timing, order-dependent) | Erode trust in the test suite | Use deterministic assertions, isolate test state |
| Testing framework code | Wastes time testing third-party behavior | Only test YOUR code |
| Snapshot abuse | Large snapshots nobody reviews, break on any change | Use snapshots sparingly and review every change |
| No test isolation | Tests pass individually but fail together | Each test sets up and tears down its own state |
| Mocking everything | Tests pass but production breaks | Prefer real implementations > fakes > stubs > mocks. Mock only at boundaries where real deps are slow or non-deterministic |

## Common Rationalizations

| Rationalization | Reality |
|---|---|
| "I'll write tests after the code works" | You won't. And tests written after the fact test implementation, not behavior. |
| "This is too simple to test" | Simple code gets complicated. The test documents the expected behavior. |
| "Tests slow me down" | Tests slow you down now. They speed you up every time you change the code later. |
| "I tested it manually" | Manual testing doesn't persist. Tomorrow's change might break it with no way to know. |
| "The code is self-explanatory" | Tests ARE the specification. They document what the code should do, not what it does. |
| "It's just a prototype" | Prototypes become production code. Tests from day one prevent the "test debt" crisis. |
| "Let me run the tests again just to be extra sure" | After a clean test run, repeating the same command adds nothing unless the code has changed since. Run again after subsequent edits, not as reassurance. |

## Red Flags

- Writing code without any corresponding tests
- Tests that pass on the first run (they may not be testing what you think)
- "All tests pass" but no tests were actually run
- Bug fixes without reproduction tests
- Tests that test framework behavior instead of application behavior
- Test names that don't describe the expected behavior
- Skipping tests to make the suite pass
- Running the same test command twice in a row without any intervening code change

## Verification

After completing any implementation:

- [ ] Every new behavior has a corresponding test
- [ ] All tests pass: `npm test`
- [ ] Bug fixes include a reproduction test that failed before the fix
- [ ] Test names describe the behavior being verified
- [ ] No tests were skipped or disabled
- [ ] Coverage hasn't decreased (if tracked)

**Note:** Run each test command after a change that could affect the result. After a clean run, don't repeat the same command unless the code has changed since — re-running on unchanged code adds no confidence.