Unit Test Writer Skill

Purpose

This skill provides systematic guidance for writing comprehensive, maintainable unit tests that achieve high coverage and follow best practices across multiple programming languages.

When to Use

• Need to create unit tests for new code
• Want to improve existing test coverage
• Need guidance on test structure and organization
• Writing tests following TDD methodology
• Ensuring proper mocking and fixtures

Unit Testing Principles

Test Structure: Arrange-Act-Assert (AAA)

Every test should follow this clear pattern:

def test_feature_condition_expected():
    """Clear test description."""
    # Arrange: Setup test data and dependencies
    input_data = create_test_data()
    mock_dependency = setup_mock()

    # Act: Execute the code under test
    result = function_under_test(input_data, mock_dependency)

    # Assert: Verify expected outcomes
    assert result.status == "success"
    mock_dependency.method.assert_called_once()

Test Naming Conventions

Python (pytest):

• Pattern: test_<function>_<condition>_<expected_result>
• File: test_<source_file_name>.py
• Examples:
  • test_create_user_valid_data_returns_user
  • test_validate_email_invalid_format_raises_error
  • test_process_data_empty_input_returns_empty_list

JavaScript/TypeScript (Jest):

• Pattern: should <expected behavior> when <condition>
• File: <source_file_name>.test.ts or <source_file_name>.test.js
• Examples:
  • should return user when valid data provided
  • should throw error when email format is invalid
  • should return empty list when input is empty

Coverage Goals

• Overall coverage: ≥ 80%
• Critical business logic: ≥ 90%
• Error handling paths: 100%
• Utility functions: ≥ 85%

---

Unit Testing Workflow

1. Analyze Code to Test

Read and understand the source:

# Read the source file
cat src/module/feature.py

# Identify:
# - Functions and classes to test
# - Dependencies and external calls
# - Error conditions and edge cases
# - Input validation requirements

Checklist:

• Identify all public functions/methods
• Note external dependencies (APIs, databases, file system)
• Identify edge cases and boundary conditions
• List error conditions to test
• Understand expected behavior

Deliverable: Analysis of test requirements

---

2. Create Test File Structure

File naming:

• Python: tests/test_<module_name>.py
• JavaScript/TypeScript: tests/<ModuleName>.test.ts

Test file template:

"""
Unit tests for [module description].

Tests cover:
- [Functionality area 1]
- [Functionality area 2]
- Error handling and edge cases
"""

import pytest
from unittest.mock import Mock, MagicMock, patch
from typing import Any

from src.module.feature import (
    FunctionToTest,
    ClassToTest,
    ExceptionToRaise
)


# ============================================================================
# Fixtures
# ============================================================================

@pytest.fixture
def sample_input() -> dict[str, Any]:
    """Sample input data for tests."""
    return {
        "field1": "value1",
        "field2": 123
    }


@pytest.fixture
def mock_dependency() -> Mock:
    """Mock external dependency."""
    mock = Mock()
    mock.method.return_value = {"status": "success"}
    return mock


# ============================================================================
# Test Classes
# ============================================================================

class TestClassName:
    """Tests for ClassName functionality."""

    def test_init_valid_params_creates_instance(self):
        """Test initialization with valid parameters."""
        pass

    def test_method_valid_input_returns_expected(self):
        """Test method with valid input."""
        pass


# ============================================================================
# Test Functions
# ============================================================================

def test_function_valid_input_returns_expected():
    """Test function with valid input."""
    pass


def test_function_invalid_input_raises_error():
    """Test function with invalid input raises error."""
    pass

Deliverable: Structured test file

---

3. Write Test Cases

Test coverage checklist:

• Happy path: Normal successful execution
• Edge cases: Boundary conditions (empty, null, max values)
• Error cases: Invalid input, exceptions
• State changes: Verify object state after operations
• Side effects: Verify external calls (mocked)
• Return values: Verify correct outputs
• Data validation: Input validation tests

Example test cases:

def test_create_user_valid_data_returns_user(sample_input, mock_db):
    """Test user creation with valid data."""
    # Arrange
    service = UserService(database=mock_db)

    # Act
    user = service.create_user(sample_input)

    # Assert
    assert user.name == sample_input["name"]
    assert user.email == sample_input["email"]
    mock_db.save.assert_called_once()


def test_create_user_duplicate_email_raises_error(mock_db):
    """Test user creation with duplicate email raises error."""
    # Arrange
    service = UserService(database=mock_db)
    mock_db.exists.return_value = True

    # Act & Assert
    with pytest.raises(DuplicateEmailError):
        service.create_user({"email": "[email protected]"})


def test_create_user_invalid_email_raises_error():
    """Test user creation with invalid email format."""
    # Arrange
    service = UserService()
    invalid_data = {"email": "not-an-email"}

    # Act & Assert
    with pytest.raises(ValidationError, match="Invalid email"):
        service.create_user(invalid_data)


@pytest.mark.parametrize("email,valid", [
    ("[email protected]", True),
    ("invalid.email", False),
    ("", False),
    (None, False),
    ("@example.com", False),
    ("user@", False),
])
def test_email_validation(email, valid):
    """Test email validation with various inputs."""
    # Act
    result = validate_email(email)

    # Assert
    assert result == valid

Deliverable: Comprehensive test cases

---

4. Implement Mocking

When to mock:

• External API calls
• Database operations
• File system operations
• Time-dependent operations
• Random number generation
• External services

Mocking patterns:

# Mock with unittest.mock
from unittest.mock import Mock, MagicMock, patch

# Method 1: Mock passed as argument
def test_with_mock_argument(mock_dependency):
    service = Service(dependency=mock_dependency)
    result = service.process()
    mock_dependency.method.assert_called_once()


# Method 2: Patch decorator
@patch('module.external_function')
def test_with_patch(mock_external):
    mock_external.return_value = "expected"
    result = function_using_external()
    assert result == "expected"


# Method 3: Context manager
def test_with_context_manager():
    with patch('module.external_function') as mock_func:
        mock_func.return_value = "expected"
        result = function_using_external()
        assert result == "expected"


# Mock side effects
def test_with_side_effect():
    mock = Mock()
    mock.method.side_effect = [1, 2, 3]  # Returns different values

    assert mock.method() == 1
    assert mock.method() == 2
    assert mock.method() == 3


# Mock exceptions
def test_with_exception():
    mock = Mock()
    mock.method.side_effect = ValueError("Error message")

    with pytest.raises(ValueError):
        mock.method()

Deliverable: Properly mocked tests

---

5. Write Fixtures

Fixture patterns:

# conftest.py - Shared fixtures

import pytest
from pathlib import Path


@pytest.fixture
def sample_data() -> dict:
    """Sample data for tests."""
    return {
        "id": 1,
        "name": "test",
        "value": 123
    }


@pytest.fixture
def temp_directory(tmp_path: Path) -> Path:
    """Temporary directory for test files."""
    test_dir = tmp_path / "test_data"
    test_dir.mkdir()
    return test_dir


@pytest.fixture
def mock_database() -> Mock:
    """Mock database connection."""
    mock_db = Mock()
    mock_db.connect.return_value = True
    mock_db.execute.return_value = []
    return mock_db


@pytest.fixture
def user_service(mock_database) -> UserService:
    """UserService with mocked database."""
    return UserService(database=mock_database)


# Fixture with setup and teardown
@pytest.fixture
def setup_environment():
    """Setup test environment."""
    # Setup
    original_value = os.environ.get("TEST_VAR")
    os.environ["TEST_VAR"] = "test_value"

    yield  # Test runs here

    # Teardown
    if original_value:
        os.environ["TEST_VAR"] = original_value
    else:
        del os.environ["TEST_VAR"]


# Parametrized fixture
@pytest.fixture(params=["value1", "value2", "value3"])
def test_values(request):
    """Parametrized fixture for multiple test values."""
    return request.param

Deliverable: Reusable test fixtures

---

6. Test Async Code

Python async tests:

import pytest
import asyncio


@pytest.mark.asyncio
async def test_async_function():
    """Test async function."""
    # Arrange
    input_data = {"key": "value"}

    # Act
    result = await async_function(input_data)

    # Assert
    assert result.success is True


@pytest.mark.asyncio
async def test_async_with_mock():
    """Test async function with mock."""
    # Arrange
    mock_api = Mock()
    mock_api.fetch = AsyncMock(return_value={"data": "test"})

    # Act
    result = await process_with_api(mock_api)

    # Assert
    assert result["data"] == "test"
    mock_api.fetch.assert_called_once()

JavaScript/TypeScript async tests:

describe('async operations', () => {
  it('should resolve with expected result', async () => {
    // Arrange
    const input = { key: 'value' };

    // Act
    const result = await asyncFunction(input);

    // Assert
    expect(result.success).toBe(true);
  });

  it('should reject with error', async () => {
    // Arrange
    const invalidInput = null;

    // Act & Assert
    await expect(asyncFunction(invalidInput)).rejects.toThrow('Invalid input');
  });
});

Deliverable: Tested async operations

---

7. Run Tests and Check Coverage

Run tests:

# Python (pytest)
pytest tests/ -v
pytest tests/test_feature.py -v
pytest tests/test_feature.py::test_specific_test -v

# With coverage
pytest tests/ --cov=src --cov-report=html --cov-report=term-missing

# JavaScript/TypeScript (Jest)
npm test
jest tests/Feature.test.ts
jest --coverage

Check coverage report:

# Python - View HTML coverage report
open htmlcov/index.html

# Identify uncovered lines
pytest --cov=src --cov-report=term-missing

# JavaScript - View coverage
open coverage/lcov-report/index.html

Coverage checklist:

• Overall coverage ≥ 80%
• All critical paths covered
• All error conditions tested
• All public APIs tested
• No untested branches in critical code

Deliverable: Coverage report with ≥ 80% coverage

---

Testing Best Practices

1. Test Independence

Each test should be independent:

# Good: Independent tests
def test_create_user():
    user = create_user({"name": "Alice"})
    assert user.name == "Alice"

def test_delete_user():
    user = create_user({"name": "Bob"})
    delete_user(user.id)
    assert get_user(user.id) is None


# Bad: Tests depend on each other
def test_create_user():
    global created_user
    created_user = create_user({"name": "Alice"})

def test_delete_user():
    # Depends on test_create_user running first
    delete_user(created_user.id)

2. Clear Test Names

Descriptive test names:

# Good: Clear and descriptive
def test_create_user_with_valid_email_returns_user():
    pass

def test_create_user_with_duplicate_email_raises_duplicate_error():
    pass


# Bad: Unclear names
def test_user1():
    pass

def test_error():
    pass

3. One Assertion Per Test (Generally)

# Good: Tests one thing
def test_user_creation_sets_name():
    user = create_user({"name": "Alice"})
    assert user.name == "Alice"

def test_user_creation_generates_id():
    user = create_user({"name": "Alice"})
    assert user.id is not None


# Acceptable: Related assertions
def test_user_creation_returns_user_with_attributes():
    user = create_user({"name": "Alice", "email": "[email protected]"})
    assert user.name == "Alice"
    assert user.email == "[email protected]"
    assert user.id is not None

4. Test Behavior, Not Implementation

# Good: Tests behavior
def test_user_validation_rejects_invalid_email():
    with pytest.raises(ValidationError):
        validate_user({"email": "invalid"})


# Bad: Tests implementation details
def test_user_validation_calls_email_regex():
    # Don't test that specific internal method is called
    validator = UserValidator()
    validator.validate({"email": "[email protected]"})
    assert validator._email_regex_called is True

5. Use Descriptive Assertion Messages

# Good: Clear failure messages
assert len(users) == 3, f"Expected 3 users, got {len(users)}"
assert user.is_active, f"User {user.id} should be active"


# Better: Use pytest assertion introspection
assert len(users) == 3  # pytest shows actual vs expected

---

Common Patterns

Testing Exceptions

# Method 1: pytest.raises context manager
def test_raises_value_error():
    with pytest.raises(ValueError):
        function_that_raises()

# Method 2: With message matching
def test_raises_specific_error():
    with pytest.raises(ValueError, match="Invalid input"):
        function_that_raises()

# Method 3: Capturing exception for inspection
def test_exception_details():
    with pytest.raises(CustomError) as exc_info:
        function_that_raises()

    assert exc_info.value.code == 400
    assert "field" in exc_info.value.details

Parametrized Tests

@pytest.mark.parametrize("input_value,expected", [
    ("[email protected]", True),
    ("invalid.email", False),
    ("", False),
    ("no@domain", False),
    ("@no-user.com", False),
])
def test_email_validation(input_value, expected):
    """Test email validation with multiple inputs."""
    result = validate_email(input_value)
    assert result == expected


@pytest.mark.parametrize("user_type,can_delete", [
    ("admin", True),
    ("moderator", True),
    ("user", False),
    ("guest", False),
])
def test_deletion_permissions(user_type, can_delete):
    """Test deletion permissions by user type."""
    user = User(type=user_type)
    assert user.can_delete() == can_delete

Testing File Operations

def test_save_file(tmp_path):
    """Test file saving."""
    # Arrange
    file_path = tmp_path / "test_file.txt"
    content = "test content"

    # Act
    save_file(file_path, content)

    # Assert
    assert file_path.exists()
    assert file_path.read_text() == content


def test_read_file(tmp_path):
    """Test file reading."""
    # Arrange
    file_path = tmp_path / "test_file.txt"
    file_path.write_text("test content")

    # Act
    content = read_file(file_path)

    # Assert
    assert content == "test content"

Testing Time-Dependent Code

from datetime import datetime, timedelta
from unittest.mock import patch

@patch('module.datetime')
def test_time_based_function(mock_datetime):
    """Test function that depends on current time."""
    # Arrange
    fixed_time = datetime(2024, 1, 1, 12, 0, 0)
    mock_datetime.now.return_value = fixed_time

    # Act
    result = get_expiration_time()

    # Assert
    expected = fixed_time + timedelta(days=30)
    assert result == expected

---

Supporting Resources

Refer to these additional files for specific guidance:

• testing-patterns.md: Common testing patterns and examples
• mocking-guide.md: Comprehensive mocking strategies
• coverage-guide.md: Coverage analysis and improvement strategies

---

Integration with Testing Workflow

Input: Source code to test Process: Analyze → Structure → Write tests → Mock → Run → Verify Output: Comprehensive unit test suite with ≥ 80% coverage Next Step: Integration testing or validation

---

Quality Checklist

Before completing unit testing:

• All public functions/methods have tests
• Test naming follows conventions
• Tests follow Arrange-Act-Assert pattern
• External dependencies are mocked
• Edge cases are tested
• Error conditions are tested
• Tests are independent
• All tests pass
• Coverage ≥ 80%
• No flaky tests
• Tests run quickly (< 1 minute)
• Clear test documentation

---

Remember

• Write tests first (TDD: Red → Green → Refactor)
• Test behavior, not implementation
• Mock external dependencies
• Keep tests simple and focused
• Use descriptive names
• Aim for high coverage of critical paths
• Tests are documentation - make them readable
• Fast tests = productive development