test-driven-development

name	test-driven-development
description	Use when implementing any feature or bugfix, before writing implementation code

Test-Driven Development (TDD)

Overview

Write the test first. Watch it fail. Write minimal code to pass.

Core principle: If you didn't watch the test fail, you don't know if it tests the right thing.

Violating the letter of the rules is violating the spirit of the rules.

When to Use

Always:

• New features
• Bug fixes
• Refactoring
• Behavior changes

Exceptions (ask your human partner):

• Throwaway prototypes
• Generated code
• Configuration files

Thinking "skip TDD just this once"? Stop. That's rationalization.

The Iron Law

NO PRODUCTION CODE WITHOUT A FAILING TEST FIRST

Write code before the test? Delete it. Start over.

No exceptions:

• Don't keep it as "reference"
• Don't "adapt" it while writing tests
• Don't look at it
• Delete means delete

Implement fresh from tests. Period.

Before RED Phase - Understand Context

BEFORE writing your first test, understand what you're building:

Quick Context Checklist (2-3 minutes)

• Read existing code - Are there similar features? What patterns do they use?
• Search for utilities - Does validation/error handling/formatting already exist?
• Check data flow - How does data enter/exit this part of the system?
• Review constraints - What requirements, edge cases, or limitations exist?

Why this matters: Writing tests without context = testing the wrong thing or duplicating existing code.

Example:

// ❌ BAD: Jump straight to test without checking codebase
test('validates email format', () => {
  expect(isValidEmail('[e-mail]')).toBe(true);
});
// Codebase already has EmailValidator class you didn't find!

// ✅ GOOD: After 2-min search, found existing validator
test('validates email using EmailValidator', () => {
  const validator = new EmailValidator();
  expect(validator.isValid('[e-mail]')).toBe(true);
});
// Consistent with codebase patterns

When to skip: Greenfield projects with no existing code. Otherwise, always understand context first.

---

Implementation Discipline

After writing tests and minimal code, maintain quality through these practices:

Consistency and Patterns

Principle	Guideline	Example
Follow established patterns	Match existing code style, structure, organization	✅ Use same error handling pattern as rest of codebase ❌ Introduce new error pattern
Maintain consistency	Same naming conventions, file structure, module organization	✅ getUserById() matches getPostById() ❌ fetchUser() when others use get*()
Keep functions focused	Single responsibility, one clear purpose	✅ validateEmail() ❌ validateAndSaveEmail()
Prefer autonomous methods	Methods that don't rely on external state	✅ Pure functions when possible ❌ Hidden dependencies

Completion Checklist

After implementing each feature:

• Verify tests pass - Run full test suite, not just new tests
• Update TODO/CHANGELOG - Mark task complete, document change
• Update documentation - README, API docs, inline comments (WHY not WHAT)
• Check consistency - Does new code match existing patterns?
• Review dependencies - No unnecessary coupling introduced?

Redundancy Elimination

Avoid redundant work by understanding upfront:

Type	Avoid	Instead
Comments	Explain WHAT code does (readable from code)	Explain WHY (business logic, tradeoffs, gotchas)
Tests	Multiple tests for same behavior	Consolidate similar tests, use parameterized tests
Code	Duplicate implementations	Understand constraints first, write once correctly
Rewrites	Multiple attempts due to misunderstanding	Read existing code, clarify requirements BEFORE coding

Example:

// ❌ BAD: Comment explains WHAT (redundant with code)
// Check if user is authenticated
if (user.token && user.token.expiresAt > Date.now()) {
  // ...
}

// ✅ GOOD: Comment explains WHY (adds context)
// Must check token expiry here, not in middleware, because
// admin routes bypass middleware but still need validation
if (user.token && user.token.expiresAt > Date.now()) {
  // ...
}

Test consolidation:

// ❌ BAD: Redundant tests
test('validates email with @', () => { /*...*/ });
test('validates email with domain', () => { /*...*/ });
test('validates email with TLD', () => { /*...*/ });

// ✅ GOOD: Parameterized test
test.each([
  ['[e-mail]', true],
  ['missing-at.com', false],
  ['no-domain@', false],
  ['no-tld@example', false],
])('validates email format: %s → %s', (email, expected) => {
  expect(isValidEmail(email)).toBe(expected);
});

---

Red-Green-Refactor

flowchart LR
    A[UNDERSTAND<br/>Context check]
    B[RED<br/>Write failing test]
    C{Verify fails<br/>correctly}
    D[GREEN<br/>Minimal code]
    E{Verify passes<br/>All green}
    F[REFACTOR<br/>Clean up]
    G((Next))
    
    A --> B
    B --> C
    C -->|yes| D
    C -->|wrong failure| B
    D --> E
    E -->|yes| F
    E -->|no| D
    F -->|stay green| E
    E -->|next| G
    G --> B
    
    style A fill:#ffffcc
    style B fill:#ffcccc
    style D fill:#ccffcc
    style F fill:#ccccff

RED - Write Failing Test

Write one minimal test showing what should happen.

```typescript test('retries failed operations 3 times', async () => { let attempts = 0; const operation = () => { attempts++; if (attempts < 3) throw new Error('fail'); return 'success'; };

const result = await retryOperation(operation);

expect(result).toBe('success'); expect(attempts).toBe(3); });

Clear name, tests real behavior, one thing
</Good>

<Bad>
```typescript
test('retry works', async () => {
  const mock = jest.fn()
    .mockRejectedValueOnce(new Error())
    .mockRejectedValueOnce(new Error())
    .mockResolvedValueOnce('success');
  await retryOperation(mock);
  expect(mock).toHaveBeenCalledTimes(3);
});

Vague name, tests mock not code

Requirements:

• One behavior
• Clear name
• Real code (no mocks unless unavoidable)

Verify RED - Watch It Fail

MANDATORY. Never skip.

npm test path/to/test.test.ts

Confirm:

• Test fails (not errors)
• Failure message is expected
• Fails because feature missing (not typos)

Test passes? You're testing existing behavior. Fix test.

Test errors? Fix error, re-run until it fails correctly.

GREEN - Minimal Code

Write simplest code to pass the test.

```typescript async function retryOperation(fn: () => Promise): Promise { for (let i = 0; i < 3; i++) { try { return await fn(); } catch (e) { if (i === 2) throw e; } } throw new Error('unreachable'); } ``` Just enough to pass ```typescript async function retryOperation( fn: () => Promise, options?: { maxRetries?: number; backoff?: 'linear' | 'exponential'; onRetry?: (attempt: number) => void; } ): Promise { // YAGNI } ``` Over-engineered

Don't add features, refactor other code, or "improve" beyond the test.

Verify GREEN - Watch It Pass

MANDATORY.

npm test path/to/test.test.ts

Confirm:

• Test passes
• Other tests still pass
• Output pristine (no errors, warnings)

Test fails? Fix code, not test.

Other tests fail? Fix now.

REFACTOR - Clean Up

After green only:

• Remove duplication
• Improve names
• Extract helpers

Keep tests green. Don't add behavior.

Repeat

Next failing test for next feature.

Good Tests

Quality	Good	Bad
Minimal	One thing. "and" in name? Split it.	test('validates email and domain and whitespace')
Clear	Name describes behavior	test('test1')
Shows intent	Demonstrates desired API	Obscures what code should do

Testing Philosophy

Test data should mirror production reality:

Production-Quality Test Data

Principle	Guideline	Example
Mirrors production	Use realistic data structures, values, formats	✅ {email: "[e-mail]", age: 28} ❌ {email: "x", age: 1}
Full transformation chain	Test data through entire processing pipeline	✅ Input → validate → transform → store → retrieve ❌ Test only transform step
Satisfies all constraints	Data passes ALL validation rules	✅ Check required fields, formats, ranges ❌ Only test happy path
Includes edge cases	Empty strings, nulls, boundary values, special chars	✅ ["", null, 0, -1, "🔥"] ❌ Only "test"

Why this matters: Tests with toy data (x, y, foo, bar) pass but real data fails. Production-quality test data catches real bugs.

Example:

// ❌ BAD: Toy data that doesn't reflect reality
test('processes user', () => {
  const user = { name: 'x', email: 'y' };
  expect(process(user)).toBeTruthy();
});
// Missing: age validation, email format, required fields

// ✅ GOOD: Production-realistic data
test('processes valid user through full pipeline', () => {
  const user = {
    name: 'Alice Smith',
    email: '[e-mail]',
    age: 28,
    preferences: { newsletter: true }
  };
  
  const result = process(user);
  
  expect(result.id).toMatch(/^usr_[a-z0-9]+$/);
  expect(result.name).toBe('Alice Smith');
  expect(result.email).toBe('[e-mail]');
});

test('rejects user with invalid email format', () => {
  const user = {
    name: 'Bob Jones',
    email: 'not-an-email',  // Edge case: malformed
    age: 35
  };
  
  expect(() => process(user)).toThrow('Invalid email format');
});

test('handles edge cases in user data', () => {
  const edgeCases = [
    { name: '', email: '[e-mail]', age: 18 },      // Empty name
    { name: 'Test', email: '', age: 18 },                  // Empty email  
    { name: 'Test', email: '[e-mail]', age: 0 },   // Boundary age
    { name: 'Test', email: '[e-mail]', age: null }, // Null age
  ];
  
  edgeCases.forEach(user => {
    expect(() => process(user)).toThrow();
  });
});

Test Coverage Checklist

• Happy path - Valid data through full flow
• Validation - Each constraint violation caught
• Edge cases - Empty, null, zero, boundary values
• Error cases - Invalid formats, missing fields, out-of-range
• Data transformation - Input → output verified at each step
• State changes - Side effects (DB writes, API calls) verified

Why Order Matters

"I'll write tests after to verify it works"

Tests written after code pass immediately. Passing immediately proves nothing:

• Might test wrong thing
• Might test implementation, not behavior
• Might miss edge cases you forgot
• You never saw it catch the bug

Test-first forces you to see the test fail, proving it actually tests something.

"I already manually tested all the edge cases"

Manual testing is ad-hoc. You think you tested everything but:

• No record of what you tested
• Can't re-run when code changes
• Easy to forget cases under pressure
• "It worked when I tried it" ≠ comprehensive

Automated tests are systematic. They run the same way every time.

"Deleting X hours of work is wasteful"

Sunk cost fallacy. The time is already gone. Your choice now:

• Delete and rewrite with TDD (X more hours, high confidence)
• Keep it and add tests after (30 min, low confidence, likely bugs)

The "waste" is keeping code you can't trust. Working code without real tests is technical debt.

"TDD is dogmatic, being pragmatic means adapting"

TDD IS pragmatic:

• Finds bugs before commit (faster than debugging after)
• Prevents regressions (tests catch breaks immediately)
• Documents behavior (tests show how to use code)
• Enables refactoring (change freely, tests catch breaks)

"Pragmatic" shortcuts = debugging in production = slower.

"Tests after achieve the same goals - it's spirit not ritual"

No. Tests-after answer "What does this do?" Tests-first answer "What should this do?"

Tests-after are biased by your implementation. You test what you built, not what's required. You verify remembered edge cases, not discovered ones.

Tests-first force edge case discovery before implementing. Tests-after verify you remembered everything (you didn't).

30 minutes of tests after ≠ TDD. You get coverage, lose proof tests work.

Common Rationalizations

Excuse	Reality
"Too simple to test"	Simple code breaks. Test takes 30 seconds.
"I'll test after"	Tests passing immediately prove nothing.
"Tests after achieve same goals"	Tests-after = "what does this do?" Tests-first = "what should this do?"
"Already manually tested"	Ad-hoc ≠ systematic. No record, can't re-run.
"Deleting X hours is wasteful"	Sunk cost fallacy. Keeping unverified code is technical debt.
"Keep as reference, write tests first"	You'll adapt it. That's testing after. Delete means delete.
"Need to explore first"	Fine. Throw away exploration, start with TDD.
"Test hard = design unclear"	Listen to test. Hard to test = hard to use.
"TDD will slow me down"	TDD faster than debugging. Pragmatic = test-first.
"Manual test faster"	Manual doesn't prove edge cases. You'll re-test every change.
"Existing code has no tests"	You're improving it. Add tests for existing code.

Red Flags - STOP and Start Over

• Code before test
• Test after implementation
• Test passes immediately
• Can't explain why test failed
• Tests added "later"
• Rationalizing "just this once"
• "I already manually tested it"
• "Tests after achieve the same purpose"
• "It's about spirit not ritual"
• "Keep as reference" or "adapt existing code"
• "Already spent X hours, deleting is wasteful"
• "TDD is dogmatic, I'm being pragmatic"
• "This is different because..."

All of these mean: Delete code. Start over with TDD.

Example: Bug Fix

Bug: Empty email accepted

RED

test('rejects empty email', async () => {
  const result = await submitForm({ email: '' });
  expect(result.error).toBe('Email required');
});

Verify RED

$ npm test
FAIL: expected 'Email required', got undefined

GREEN

function submitForm(data: FormData) {
  if (!data.email?.trim()) {
    return { error: 'Email required' };
  }
  // ...
}

Verify GREEN

$ npm test
PASS

REFACTOR Extract validation for multiple fields if needed.

Verification Checklist

Before marking work complete:

• Every new function/method has a test
• Watched each test fail before implementing
• Each test failed for expected reason (feature missing, not typo)
• Wrote minimal code to pass each test
• All tests pass
• Output pristine (no errors, warnings)
• Tests use real code (mocks only if unavoidable)
• Edge cases and errors covered

Can't check all boxes? You skipped TDD. Start over.

When Stuck

Problem	Solution
Don't know how to test	Write wished-for API. Write assertion first. Ask your human partner.
Test too complicated	Design too complicated. Simplify interface.
Must mock everything	Code too coupled. Use dependency injection.
Test setup huge	Extract helpers. Still complex? Simplify design.

Debugging Integration

Bug found? Write failing test reproducing it. Follow TDD cycle. Test proves fix and prevents regression.

Never fix bugs without a test.

Testing Anti-Patterns

When adding mocks or test utilities, read testing-anti-patterns.md to avoid common pitfalls:

• Testing mock behavior instead of real behavior
• Adding test-only methods to production classes
• Mocking without understanding dependencies

Final Rule

Production code → test exists and failed first
Otherwise → not TDD

No exceptions without your human partner's permission.