test-driven-development

name	test-driven-development
description	Comprehensive TDD patterns and practices for all programming languages, eliminating redundant testing guidance per agent.
updated_at	"2025-10-30T17:00:00.000Z"
tags	["testing","tdd","best-practices","quality-assurance"]

Test-Driven Development (TDD)

Comprehensive TDD patterns and practices for all programming languages. This skill eliminates ~500-800 lines of redundant testing guidance per agent.

When to Use

Apply TDD for:

• New feature implementation
• Bug fixes (test the bug first)
• Code refactoring (tests ensure behavior preservation)
• API development (test contracts)
• Complex business logic

TDD Workflow (Red-Green-Refactor)

1. Red Phase: Write Failing Test

Write a test that:
- Describes the desired behavior
- Fails for the right reason (not due to syntax errors)
- Is focused on a single behavior

2. Green Phase: Make It Pass

Write the minimum code to:
- Pass the test
- Not introduce regressions
- Follow existing patterns

3. Refactor Phase: Improve Code

While keeping tests green:
- Remove duplication
- Improve naming
- Simplify logic
- Extract functions/classes

Test Structure Patterns

Arrange-Act-Assert (AAA)

// Arrange: Set up test data and conditions
const user = createTestUser({ role: 'admin' });

// Act: Perform the action being tested
const result = await authenticateUser(user);

// Assert: Verify the outcome
expect(result.isAuthenticated).toBe(true);
expect(result.permissions).toContain('admin');

Given-When-Then (BDD Style)

Given: A user with admin privileges
When: They attempt to access protected resource
Then: Access is granted with appropriate permissions

Test Naming Conventions

Pattern: test_should_<expected_behavior>_when_<condition>

Examples:

• test_should_return_user_when_id_exists()
• test_should_raise_error_when_user_not_found()
• test_should_validate_email_format_when_creating_account()

Language-Specific Conventions

Python (pytest):

def test_should_calculate_total_when_items_added():
    # Arrange
    cart = ShoppingCart()
    cart.add_item(Item("Book", 10.00))
    cart.add_item(Item("Pen", 1.50))

    # Act
    total = cart.calculate_total()

    # Assert
    assert total == 11.50

JavaScript (Jest):

describe('ShoppingCart', () => {
  test('should calculate total when items added', () => {
    const cart = new ShoppingCart();
    cart.addItem({ name: 'Book', price: 10.00 });
    cart.addItem({ name: 'Pen', price: 1.50 });

    const total = cart.calculateTotal();

    expect(total).toBe(11.50);
  });
});

Go:

func TestShouldCalculateTotalWhenItemsAdded(t *testing.T) {
    // Arrange
    cart := NewShoppingCart()
    cart.AddItem(Item{Name: "Book", Price: 10.00})
    cart.AddItem(Item{Name: "Pen", Price: 1.50})

    // Act
    total := cart.CalculateTotal()

    // Assert
    if total != 11.50 {
        t.Errorf("Expected 11.50, got %f", total)
    }
}

Test Types and Scope

Unit Tests

• Scope: Single function/method
• Dependencies: Mocked
• Speed: Fast (< 10ms per test)
• Coverage: 80%+ of code paths

Integration Tests

• Scope: Multiple components
• Dependencies: Real or test doubles
• Speed: Moderate (< 1s per test)
• Coverage: Critical paths and interfaces

End-to-End Tests

• Scope: Full user workflows
• Dependencies: Real (in test environment)
• Speed: Slow (seconds to minutes)
• Coverage: Core user journeys

Mocking and Test Doubles

When to Mock

• External APIs and services
• Database operations (for unit tests)
• File system operations
• Time-dependent operations
• Random number generation

Mock Types

Stub: Returns predefined data

def get_user_stub(user_id):
    return User(id=user_id, name="Test User")

Mock: Verifies interactions

mock_service = Mock()
service.process_payment(payment_data)
mock_service.process_payment.assert_called_once_with(payment_data)

Fake: Working implementation (simplified)

class FakeDatabase:
    def __init__(self):
        self.data = {}

    def save(self, key, value):
        self.data[key] = value

    def get(self, key):
        return self.data.get(key)

Test Coverage Guidelines

Target Coverage Levels

• Critical paths: 100%
• Business logic: 95%+
• Overall project: 80%+
• UI components: 70%+

What to Test

• ✅ Business logic and algorithms
• ✅ Edge cases and boundary conditions
• ✅ Error handling and validation
• ✅ State transitions
• ✅ Public APIs and interfaces

What NOT to Test

• ❌ Framework internals
• ❌ Third-party libraries
• ❌ Trivial getters/setters
• ❌ Generated code
• ❌ Configuration files

Testing Best Practices

1. One Assertion Per Test (When Possible)

# Good: Focused test
def test_should_validate_email_format():
    assert is_valid_email("[e-mail]") is True

# Avoid: Multiple unrelated assertions
def test_validation():
    assert is_valid_email("[e-mail]") is True
    assert is_valid_phone("123-456-7890") is True  # Different concept

2. Test Independence

# Good: Each test is self-contained
def test_user_creation():
    user = create_user("[e-mail]")
    assert user.email == "[e-mail]"

# Avoid: Tests depending on execution order
shared_user = None

def test_create_user():
    global shared_user
    shared_user = create_user("[e-mail]")

def test_update_user():  # Depends on previous test
    shared_user.name = "Updated"

3. Descriptive Test Failures

# Good: Clear failure message
assert result.status == 200, f"Expected 200, got {result.status}: {result.body}"

# Avoid: Unclear failure
assert result.status == 200

4. Test Data Builders

# Good: Reusable test data creation
def create_test_user(**overrides):
    defaults = {
        'email': '[e-mail]',
        'name': 'Test User',
        'role': 'user'
    }
    return User(**{**defaults, **overrides})

# Usage
admin = create_test_user(role='admin')
guest = create_test_user(email='[e-mail]')

Testing Anti-Patterns to Avoid

❌ Testing Implementation Details

# Bad: Tests internal structure
def test_user_storage():
    user = User("[e-mail]")
    assert user._internal_cache is not None  # Implementation detail

❌ Fragile Tests

# Bad: Breaks with harmless changes
assert user.to_json() == '{"name":"John","email":"[e-mail]"}'

# Good: Tests behavior, not format
data = json.loads(user.to_json())
assert data['name'] == "John"
assert data['email'] == "[e-mail]"

❌ Slow Tests in Unit Test Suite

# Bad: Real HTTP calls in unit tests
def test_api_integration():
    response = requests.get("https://api.example.com/users")  # Slow!
    assert response.status_code == 200

❌ Testing Everything Through UI

# Bad: Testing business logic through UI
def test_calculation():
    browser.click("#input1")
    browser.type("5")
    browser.click("#input2")
    browser.type("3")
    browser.click("#calculate")
    assert browser.find("#result").text == "8"

# Good: Test logic directly
def test_calculation():
    assert calculate(5, 3) == 8

Quick Reference by Language

Python (pytest)

# Setup/Teardown
@pytest.fixture
def database():
    db = create_test_database()
    yield db
    db.cleanup()

# Parametrized tests
@pytest.mark.parametrize("input,expected", [
    ("[e-mail]", True),
    ("invalid-email", False),
])
def test_email_validation(input, expected):
    assert is_valid_email(input) == expected

JavaScript (Jest)

// Setup/Teardown
beforeEach(() => {
  database = createTestDatabase();
});

afterEach(() => {
  database.cleanup();
});

// Async tests
test('should fetch user data', async () => {
  const user = await fetchUser(1);
  expect(user.name).toBe('John');
});

Go

// Table-driven tests
func TestEmailValidation(t *testing.T) {
    tests := []struct {
        input    string
        expected bool
    }{
        {"[e-mail]", true},
        {"invalid-email", false},
    }

    for _, tt := range tests {
        result := IsValidEmail(tt.input)
        if result != tt.expected {
            t.Errorf("IsValidEmail(%s) = %v, want %v",
                tt.input, result, tt.expected)
        }
    }
}

TDD Benefits Realized

• Design Improvement: Tests drive better API design
• Documentation: Tests serve as executable documentation
• Confidence: Refactoring becomes safe
• Debugging: Tests isolate issues quickly
• Coverage: Ensures comprehensive test coverage
• Regression Prevention: Catches bugs before deployment

Related Skills

When using Test Driven Development, these skills enhance your workflow:

• systematic-debugging: Debug-first methodology when tests fail unexpectedly
• react: Testing React components, hooks, and context
• django: Testing Django models, views, and forms
• fastapi-local-dev: Testing FastAPI endpoints and dependency injection

[Full documentation available in these skills if deployed in your bundle]