Testing Workflow

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in RFC 2119.

Testing Workflow

Testing workflow patterns and quality standards for various frameworks and languages.

CRITICAL: Zero Warnings Tolerance

Treat all warnings as errors. No exceptions.

Pre-Commit Requirements:

• ✅ All tests pass
• ✅ Zero warnings
• ✅ No linting errors
• ✅ No type errors
• ✅ Code formatted

MUST NOT commit with:

• ❌ Failing tests
• ❌ Any warnings
• ❌ Linting errors
• ❌ Unformatted code

Testing Strategy

Test Pyramid

1. Unit Tests (70%) - Fast, isolated, test individual functions/classes
2. Integration Tests (20%) - Test component interactions
3. End-to-End Tests (10%) - Full system tests

What to Test

✅ DO test:

• Public APIs and interfaces
• Business logic and calculations
• Edge cases (empty inputs, None values, boundaries)
• Error handling and exceptions
• Data validation
• Critical paths through the application

❌ DON'T test:

• Private implementation details
• Third-party library internals
• Trivial getters/setters
• Framework magic (unless you suspect bugs)

Development Workflow

During Development:

• Run targeted tests for fast iteration
• Fix issues immediately

Before Commit:

• Run full suite
• Fix all warnings/errors
• May run 1500+ tests

Test Organization

Directory Structure

tests/
├── __init__.py / conftest.py  # Shared fixtures and setup
├── unit/                      # Fast, isolated tests
│   └── test_*.py
├── integration/               # Component interaction tests
│   └── test_*.py
└── e2e/                       # End-to-end tests
    └── test_*.py

Naming Conventions

• Test files: test_*.py or *_test.py
• Test classes: Test* (e.g., TestUserService)
• Test functions: test_* (e.g., test_create_user_success)
• Fixtures: Descriptive names (e.g., user_service, mock_database)

CRITICAL: MUST NOT name non-test classes with "Test" prefix - framework will try to collect them as tests.

Coverage Requirements

Coverage Goals

• Minimum: 80% overall coverage
• Critical paths: 100% coverage
• New code: Should not decrease overall coverage
• Focus: Behavior over line count

What to Cover

Test these:

• Business logic, algorithms
• Edge cases, boundary conditions
• Error handling and exceptions
• Integration points, APIs
• Data validation
• Security-critical paths

Skip these:

• Trivial getters/setters
• Private implementation details
• Third-party library internals
• Simple data classes
• Framework magic (unless suspected bug)

Edge Cases to Test

• Empty inputs (empty strings, empty collections)
• None/null values
• Boundary values (zero, maximum, minimum)
• Single item cases
• Negative numbers
• Large numbers
• Mixed positive and negative values

Test Structure - Arrange-Act-Assert Pattern

All tests follow the Arrange-Act-Assert (AAA) pattern for clarity:

1. Arrange - Set up test data and conditions
2. Act - Execute the functionality being tested
3. Assert - Verify the results

This structure makes tests:

• Easy to understand at a glance
• Simple to maintain
• Consistent across the codebase

Python Example

# ✅ GOOD - Clear AAA structure
def test_user_registration():
    # Arrange
    user_data = {"email": "test@example.com", "password": "secure"}
    # Act
    result = register_user(user_data)
    # Assert
    assert result.success
    assert result.user.email == "test@example.com"

# ❌ BAD - Testing implementation details
def test_internal_method():
    obj = MyClass()
    assert obj._internal_state == expected  # Don't test private state

Fixtures

# conftest.py
@pytest.fixture
def db_connection():
    conn = create_test_database()
    yield conn
    conn.close()

@pytest.fixture
def sample_user():
    return User(email="test@example.com", name="Test User")

# test_file.py
def test_save_user(db_connection, sample_user):
    save_user(db_connection, sample_user)
    assert user_exists(db_connection, sample_user.email)

Scopes: function (default), class, module, session

Parametrized Tests

@pytest.mark.parametrize("input,expected", [
    ("hello", "HELLO"),
    ("world", "WORLD"),
    ("", ""),
    (None, None),
])
def test_uppercase_conversion(input, expected):
    assert to_uppercase(input) == expected

Mocking

from unittest.mock import Mock, patch

# Mock external API
@patch('module.requests.get')
def test_api_call(mock_get):
    mock_get.return_value.json.return_value = {"status": "ok"}
    result = fetch_data()
    assert result["status"] == "ok"

# Dependency injection for testability
class UserService:
    def __init__(self, db_connection):
        self.db = db_connection

def test_get_user():
    mock_db = Mock()
    mock_db.query.return_value = {"id": 1, "name": "Test"}
    service = UserService(mock_db)
    assert service.get_user(1)["name"] == "Test"

Testing Exception Handling

Tests should verify that exceptions are raised with correct messages for invalid inputs:

# ✅ GOOD - Testing exception
def test_create_user_invalid_email(user_service):
    """Test user creation fails with invalid email."""
    user_data = {
        "username": "testuser",
        "email": "invalid-email",  # Invalid format
        "age": 25
    }
    # Expect exception when invalid email is provided
    with pytest.raises(ValidationError) as exc_info:
        user_service.create_user(user_data)

    assert "email" in str(exc_info.value)

Mocking External Dependencies

Tests should mock external dependencies to:

• Isolate the code being tested
• Avoid real external calls (APIs, databases, network)
• Control behavior for edge cases
• Speed up test execution

from unittest.mock import Mock, patch

# Mock external service
@patch('module.requests.get')
def test_api_call(mock_get):
    mock_get.return_value.json.return_value = {"status": "ok"}
    result = fetch_data()
    # Verify mock was called and behavior is correct
    mock_get.assert_called_once()
    assert result["status"] == "ok"

# Dependency injection for testability
class UserService:
    def __init__(self, db_connection):
        self.db = db_connection

def test_get_user():
    mock_db = Mock()
    mock_db.query.return_value = {"id": 1, "name": "Test"}
    service = UserService(mock_db)
    assert service.get_user(1)["name"] == "Test"

Integration Testing

Integration tests verify that multiple components work together correctly. They typically:

• Use real or test databases/services
• Test API endpoints with real infrastructure
• Verify component interactions
• Run slower than unit tests

@pytest.fixture(scope="module")
def test_database():
    """Provide test database for integration tests."""
    db = create_test_database()
    run_migrations(db)
    yield db
    cleanup_database(db)

def test_user_operations(test_database):
    """Test user repository with real database."""
    user = create_user(test_database, email="test@example.com")
    assert user.id is not None

    retrieved = get_user(test_database, user.id)
    assert retrieved.email == "test@example.com"

TDD Pattern Example

# 1. Write failing test
def test_calculate_discount():
    result = calculate_discount(price=100, discount_percent=10)
    assert result == 90

# 2. Implement
def calculate_discount(price, discount_percent):
    if not 0 <= discount_percent <= 100:
        raise ValueError("Discount must be between 0 and 100")
    return price - (price * discount_percent / 100)

Test Configuration

[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = ["test_*.py", "*_test.py"]
python_classes = ["Test*"]
python_functions = ["test_*"]
addopts = [
    "-v",
    "--strict-markers",
    "--tb=short",
    "--cov=app",
    "--cov-report=term-missing",
]
markers = [
    "slow: marks tests as slow (deselect with '-m \"not slow\"')",
    "integration: marks tests as integration tests",
]

Test Markers and Categorization

Tests can be categorized with markers to allow selective execution:

Common marker categories:

• slow - Tests that take longer to run (deselect with -m "not slow")
• integration - Integration tests that use external services
• unit - Unit tests (fast, isolated)
• e2e - End-to-end tests
• performance - Performance/benchmark tests

@pytest.mark.slow
def test_expensive_operation():
    result = process_large_dataset()
    assert result.success

@pytest.mark.integration
def test_database_integration():
    result = query_database()
    assert result is not None

# Run only fast tests
pytest -m "not slow"

# Run only integration tests
pytest -m integration

Performance Testing

Performance tests verify that operations complete within acceptable time limits:

import time

def test_performance_within_limit(data_processor):
    """Test processing completes within time limit."""
    large_dataset = generate_test_data(10000)

    start = time.time()
    result = data_processor.process(large_dataset)
    duration = time.time() - start

    assert duration < 1.0  # Should complete in under 1 second
    assert len(result) == 10000

Development Workflow

1. Write/modify code
2. Run targeted tests
3. Fix issues immediately
4. Run full suite (make check)
5. Commit when zero warnings

MUST:

• ✅ Test after every change
• ✅ Fix warnings immediately
• ✅ Add tests for new features

MUST NOT:

• ❌ Commit with failures/warnings
• ❌ Skip tests after changes
• ❌ Ignore failures as "known issues"

Essential Commands

# Development - Targeted
pytest tests/unit/test_file.py -v           # Specific file
pytest -k "test_name" -v                    # Pattern match
pytest tests/unit/test_file.py::test_func   # Exact test
pytest -v --tb=short                        # Cleaner errors

# Debugging
pytest -l                                   # Show locals
pytest --pdb                                # Debug on failure
pytest -x                                   # Stop on first failure
pytest --lf                                 # Rerun last failed

# Coverage
pytest --cov=app --cov-report=html          # HTML report
pytest --cov=app --cov-report=term-missing  # Terminal report

# Verification
make check                                  # Full suite + quality
uv run pytest                               # All tests
uv run black --check app/ tests/            # Format check
uv run isort --check app/ tests/            # Import order
uv run flake8 app/ tests/                   # Linting
uv run mypy app/ tests/                     # Type check

Test Quality Checklist

• ✅ Run in isolation (no shared state)
• ✅ Deterministic (same result every time)
• ✅ Fast (mock slow operations)
• ✅ Clear names document behavior
• ✅ Test edge cases and errors
• ✅ Zero warnings in output
• ✅ >80% coverage on critical paths

Advanced Testing Patterns

Out of Scope

• Python-specific pytest patterns → see python-testing
• TypeScript-specific patterns → see typescript-testing

TL;DR: Zero warnings policy. Follow test pyramid. Arrange-Act-Assert pattern. Mock external dependencies. Test behavior not implementation. >80% coverage on critical paths. Run targeted tests during development, full suite before commit.