/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.

// GOOD: Tests observable behavior
test("user can checkout with valid cart", async () => {
  const cart = createCart();
  cart.add(product);
  const result = await checkout(cart, paymentMethod);
  expect(result.status).toBe("confirmed");
});

// GOOD: Verifies through interface
test("createUser makes user retrievable", async () => {
  const user = await createUser({ name: "Alice" });
  const retrieved = await getUser(user.id);
  expect(retrieved.name).toBe("Alice");
});

Characteristics of good tests:

• Tests behavior users/callers care about
• Uses public API only
• Survives internal refactors
• Describes WHAT, not HOW
• One logical assertion per test

**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.

// BAD: Tests implementation details
test("checkout calls paymentService.process", async () => {
  const mockPayment = jest.mock(paymentService);
  await checkout(cart, payment);
  expect(mockPayment.process).toHaveBeenCalledWith(cart.total);
});

// BAD: Bypasses interface to verify
test("createUser saves to database", async () => {
  await createUser({ name: "Alice" });
  const row = await db.query("SELECT * FROM users WHERE name = ?", ["Alice"]);
  expect(row).toBeDefined();
});

Red flags:

• Mocking internal collaborators
• Testing private methods
• Asserting on call counts/order
• Test breaks when refactoring without behavior change
• Test name describes HOW not WHAT

Prefer writing tests before implementation. If you've already written code, consider starting fresh from tests rather than retrofitting — tests written after tend to verify what you built, not what's required.

Mocking

Mock at **system boundaries** only:

• External APIs (payment, email, etc.)
• Databases (sometimes - prefer test DB)
• Time/randomness
• File system (sometimes)

Don't mock:

• Your own classes/modules
• Internal collaborators
• Anything you control

**Use dependency injection** — pass external dependencies in rather than creating them internally:

// Easy to mock
function processPayment(order, paymentClient) {
  return paymentClient.charge(order.total);
}

// Hard to mock
function processPayment(order) {
  const client = new StripeClient(process.env.STRIPE_KEY);
  return client.charge(order.total);
}

**Prefer SDK-style interfaces** — specific functions for each external operation:

// GOOD: Each function is independently mockable
const api = {
  getUser: (id) => fetch(`/users/${id}`),
  getOrders: (userId) => fetch(`/users/${userId}/orders`),
  createOrder: (data) => fetch("/orders", { method: "POST", body: data }),
};

// BAD: Mocking requires conditional logic inside the mock
const api = {
  fetch: (endpoint, options) => fetch(endpoint, options),
};

Interface Design for Testability

1. **Accept dependencies, don't create them**

   // Testable
   function processOrder(order, paymentGateway) {}

   // Hard to test
   function processOrder(order) {
     const gateway = new StripeGateway();
   }

2. **Return results, don't produce side effects**

   // Testable
   function calculateDiscount(cart): Discount {}

   // Hard to test
   function applyDiscount(cart): void {
     cart.total -= discount;
   }

3. **Small surface area** — fewer methods = fewer tests needed, fewer params = simpler test setup

**Deep modules** (from "A Philosophy of Software Design"): small interface + lots of implementation. When designing, ask: Can I reduce methods? Simplify params? Hide more complexity inside?

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.

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)
• [ ] Design interfaces for testability
• [ ] List the behaviors to test (not implementation steps)
• [ ] Get user approval on the plan

Ask: "What should the public interf