koolamusic/tdd

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 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 → run test → confirm it FAILS correctly
GREEN: Write minimal code → run test → confirm it PASSES

• Test passes immediately? You're testing existing behavior. Fix the test.
• Test errors (not assertion failure)? Fix the error first — erroring is not the same as failing.

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

3. Incremental Loop

For each remaining behavior:

RED:   Write next test → run test → confirm it FAILS correctly
GREEN: Write minimal code → run test → confirm it 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:

• [ ] 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

Refactor candidates: duplication → extract function/class, long methods → break into private helpers, shallow modules → combine or deepen, feature envy → move logic to where data lives, primitive obsession → introduce value objects.

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
[ ] Watched test fail before writing code
[ ] Failure was for expected reason (missing feature, not typo)
[ ] All other tests still pass

Bug Fix Example

TDD applies to bug fixes — write a test that reproduces the bug first.

# Bug: empty email passes validation

RED:   test("rejects empty email", () => {
         const result = validateEmail("");
         expect(result.valid).toBe(false);
       });
       → Run test → FAILS (empty string passes validation) ✓

GREEN: Add check: if (!email || !email.includes("@")) return { valid: false }
       → Run test → PASSES ✓

       Verify all other validation tests still pass.

Type Testing

Compile-time type assertions. No runtime — just the project's typecheck command. Catches regressions in generics, conditional types, and type constraints that runtime tests can't see.

When: generic APIs, utility types, complex inference, mapped/conditional types, ensuring invalid usage errors. Not: trivial stuff like string prop accepts string.

Utilities

Search for a file exporting Expect and Equal. If none exists, create one:

export function Expect<T extends true>() {}
export type Equal<X, Y> = (<T>() => T extends X ? 1 : 2) extends <
  T
>() => T extends Y ? 1 : 2
  ? true
  : false;
export type Not<T extends boolean> = T extends true ? false : true;
export type IsAny<T> = 0 extends 1 & T ? true : false;
export type IsNever<T> = [T] extends [never] ? true : false;

• Expect<T extends true> — compile error = test failure
• Equal<X, Y> — exact type equality (defeats any widening)
• Not, IsAny, IsNever — edge case guards (any/never break naive comparisons)

Positive Assertions

import { Expect, Equal, Not, IsAny } from "./utils";

// Block scope each test to avoid name collisions
{
  type Result = ReturnType<typeof myGenericFn<SomeInput>>;
  Expect<Equal<Result, { id: string; name: string }>>;
  Expect<Not<IsAny<Result>>>;
}

Negative Tests

@ts-expect-error must be on the line immediately before the error. Always include a reason. Unused directive = failing test (constraint is missing).

// ✅ directive on line immediately before error
doSomething({
  // @ts-expect-error - name must be string
  name: 123,
});

// ❌ directive too far from error
doSomething({
  // @ts-expect-error - name must be string
  ...defaults,
  name: 123,
});

Tips

• declare const for mock values without runtime: declare const ctx: SomeCtx;
• type _name = Expect<...> when you need a type-level-only assertion (no runtime Expect() call needed)
• /* biome-ignore-all lint */ at file top for type-only files — suppresses unused variable warnings

Run with the project's typecheck command (npx tsc --noEmit, bun typecheck, etc.). If it compiles, it passes.