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---
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name: tdd
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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.
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---
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# Test-Driven Development
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## Philosophy
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**Core principle**: Tests should verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't.
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**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.
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**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.
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See [tests.md](tests.md) for examples and [mocking.md](mocking.md) for mocking guidelines.
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## Workflow
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### 1. Planning
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Before writing any code:
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- [ ] Confirm with user what interface changes are needed
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- [ ] Confirm with user which behaviors to test (prioritize)
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- [ ] Identify opportunities for [deep modules](deep-modules.md) (small interface, deep implementation)
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- [ ] Design interfaces for [testability](interface-design.md)
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- [ ] List the behaviors to test (not implementation steps)
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- [ ] Get user approval on the plan
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Ask: "What should the public interface look like? Which behaviors are most important to test?"
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**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.
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### 2. Tracer Bullet
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Write ONE test that confirms ONE thing about the system:
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```
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RED: Write test for first behavior → test fails
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GREEN: Write minimal code to pass → test passes
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```
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This is your tracer bullet - proves the path works end-to-end.
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### 3. Incremental Loop
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For each remaining behavior:
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```
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RED: Write next test → fails
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GREEN: Minimal code to pass → passes
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```
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Rules:
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- One test at a time
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- Only enough code to pass current test
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- Don't anticipate future tests
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- Keep tests focused on observable behavior
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### 4. Refactor
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After all tests pass, look for [refactor candidates](refactoring.md):
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- [ ] Extract duplication
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- [ ] Deepen modules (move complexity behind simple interfaces)
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- [ ] Apply SOLID principles where natural
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- [ ] Consider what new code reveals about existing code
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- [ ] Run tests after each refactor step
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**Never refactor while RED.** Get to GREEN first.
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## Checklist Per Cycle
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```
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[ ] Test describes behavior, not implementation
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[ ] Test uses public interface only
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[ ] Test would survive internal refactor
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[ ] Code is minimal for this test
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[ ] No speculative features added
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```
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# Deep Modules
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From "A Philosophy of Software Design":
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**Deep module** = small interface + lots of implementation
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```
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┌─────────────────────┐
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│ Small Interface │ ← Few methods, simple params
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├─────────────────────┤
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│ │
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│ │
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│ Deep Implementation│ ← Complex logic hidden
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│ │
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│ │
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└─────────────────────┘
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```
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**Shallow module** = large interface + little implementation (avoid)
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```
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┌─────────────────────────────────┐
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│ Large Interface │ ← Many methods, complex params
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├─────────────────────────────────┤
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│ Thin Implementation │ ← Just passes through
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└─────────────────────────────────┘
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```
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When designing interfaces, ask:
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- Can I reduce the number of methods?
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- Can I simplify the parameters?
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- Can I hide more complexity inside?
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# Interface Design for Testability
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Good interfaces make testing natural:
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1. **Accept dependencies, don't create them**
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```typescript
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// Testable
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function processOrder(order, paymentGateway) {}
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// Hard to test
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function processOrder(order) {
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const gateway = new StripeGateway();
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}
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```
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2. **Return results, don't produce side effects**
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```typescript
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// Testable
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function calculateDiscount(cart): Discount {}
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// Hard to test
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function applyDiscount(cart): void {
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cart.total -= discount;
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}
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```
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3. **Small surface area**
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- Fewer methods = fewer tests needed
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- Fewer params = simpler test setup
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# When to Mock
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Mock at **system boundaries** only:
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- External APIs (payment, email, etc.)
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- Databases (sometimes - prefer test DB)
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- Time/randomness
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- File system (sometimes)
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Don't mock:
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- Your own classes/modules
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- Internal collaborators
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- Anything you control
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## Designing for Mockability
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At system boundaries, design interfaces that are easy to mock:
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**1. Use dependency injection**
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Pass external dependencies in rather than creating them internally:
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```typescript
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// Easy to mock
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function processPayment(order, paymentClient) {
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return paymentClient.charge(order.total);
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}
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// Hard to mock
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function processPayment(order) {
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const client = new StripeClient(process.env.STRIPE_KEY);
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return client.charge(order.total);
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}
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```
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**2. Prefer SDK-style interfaces over generic fetchers**
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Create specific functions for each external operation instead of one generic function with conditional logic:
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```typescript
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// GOOD: Each function is independently mockable
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const api = {
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getUser: (id) => fetch(`/users/${id}`),
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getOrders: (userId) => fetch(`/users/${userId}/orders`),
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createOrder: (data) => fetch('/orders', { method: 'POST', body: data }),
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};
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// BAD: Mocking requires conditional logic inside the mock
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const api = {
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fetch: (endpoint, options) => fetch(endpoint, options),
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};
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```
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The SDK approach means:
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- Each mock returns one specific shape
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- No conditional logic in test setup
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- Easier to see which endpoints a test exercises
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- Type safety per endpoint
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# Refactor Candidates
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After TDD cycle, look for:
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- **Duplication** → Extract function/class
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- **Long methods** → Break into private helpers (keep tests on public interface)
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- **Shallow modules** → Combine or deepen
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- **Feature envy** → Move logic to where data lives
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- **Primitive obsession** → Introduce value objects
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- **Existing code** the new code reveals as problematic
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# Good and Bad Tests
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## Good Tests
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**Integration-style**: Test through real interfaces, not mocks of internal parts.
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```typescript
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// GOOD: Tests observable behavior
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test("user can checkout with valid cart", async () => {
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const cart = createCart();
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cart.add(product);
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const result = await checkout(cart, paymentMethod);
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expect(result.status).toBe("confirmed");
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});
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```
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Characteristics:
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- Tests behavior users/callers care about
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- Uses public API only
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- Survives internal refactors
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- Describes WHAT, not HOW
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- One logical assertion per test
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## Bad Tests
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**Implementation-detail tests**: Coupled to internal structure.
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```typescript
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// BAD: Tests implementation details
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test("checkout calls paymentService.process", async () => {
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const mockPayment = jest.mock(paymentService);
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await checkout(cart, payment);
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expect(mockPayment.process).toHaveBeenCalledWith(cart.total);
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});
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```
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Red flags:
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- Mocking internal collaborators
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- Testing private methods
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- Asserting on call counts/order
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- Test breaks when refactoring without behavior change
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- Test name describes HOW not WHAT
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- Verifying through external means instead of interface
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```typescript
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// BAD: Bypasses interface to verify
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test("createUser saves to database", async () => {
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await createUser({ name: "Alice" });
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const row = await db.query("SELECT * FROM users WHERE name = ?", ["Alice"]);
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expect(row).toBeDefined();
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});
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// GOOD: Verifies through interface
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test("createUser makes user retrievable", async () => {
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const user = await createUser({ name: "Alice" });
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const retrieved = await getUser(user.id);
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expect(retrieved.name).toBe("Alice");
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});
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```
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