Python Testing Patterns

中文描述

本技能提供全栈开发相关的最佳实践和模式指导。

name: python-testing-patterns description: Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

Python Testing Patterns

Comprehensive guide to implementing robust testing strategies in Python using pytest, fixtures, mocking, parameterization, and test-driven development practices.

When to Use This Skill

• Writing unit tests for Python code
• Setting up test suites and test infrastructure
• Implementing test-driven development (TDD)
• Creating integration tests for APIs and services
• Mocking external dependencies and services
• Testing async code and concurrent operations
• Setting up continuous testing in CI/CD
• Implementing property-based testing
• Testing database operations
• Debugging failing tests

Core Concepts

1. Test Types

• Unit Tests: Test individual functions/classes in isolation
• Integration Tests: Test interaction between components
• Functional Tests: Test complete features end-to-end
• Performance Tests: Measure speed and resource usage

2. Test Structure (AAA Pattern)

• Arrange: Set up test data and preconditions
• Act: Execute the code under test
• Assert: Verify the results

3. Test Coverage

• Measure what code is exercised by tests
• Identify untested code paths
• Aim for meaningful coverage, not just high percentages

4. Test Isolation

• Tests should be independent
• No shared state between tests
• Each test should clean up after itself

Quick Start

# test_example.py
def add(a, b):
    return a + b

def test_add():
    """Basic test example."""
    result = add(2, 3)
    assert result == 5

def test_add_negative():
    """Test with negative numbers."""
    assert add(-1, 1) == 0

# Run with: pytest test_example.py

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Testing Best Practices

Test Organization

# tests/
#   __init__.py
#   conftest.py           # Shared fixtures
#   test_unit/            # Unit tests
#     test_models.py
#     test_utils.py
#   test_integration/     # Integration tests
#     test_api.py
#     test_database.py
#   test_e2e/            # End-to-end tests
#     test_workflows.py

Test Naming Convention

A common pattern: test_<unit>_<scenario>_<expected_outcome>. Adapt to your team's preferences.

# Pattern: test_<unit>_<scenario>_<expected>
def test_create_user_with_valid_data_returns_user():
    ...

def test_create_user_with_duplicate_email_raises_conflict():
    ...

def test_get_user_with_unknown_id_returns_none():
    ...

# Good test names - clear and descriptive
def test_user_creation_with_valid_data():
    """Clear name describes what is being tested."""
    pass

def test_login_fails_with_invalid_password():
    """Name describes expected behavior."""
    pass

def test_api_returns_404_for_missing_resource():
    """Specific about inputs and expected outcomes."""
    pass

# Bad test names - avoid these
def test_1():  # Not descriptive
    pass

def test_user():  # Too vague
    pass

def test_function():  # Doesn't explain what's tested
    pass

Testing Retry Behavior

Verify that retry logic works correctly using mock side effects.

from unittest.mock import Mock

def test_retries_on_transient_error():
    """Test that service retries on transient failures."""
    client = Mock()
    # Fail twice, then succeed
    client.request.side_effect = [
        ConnectionError("Failed"),
        ConnectionError("Failed"),
        {"status": "ok"},
    ]

    service = ServiceWithRetry(client, max_retries=3)
    result = service.fetch()

    assert result == {"status": "ok"}
    assert client.request.call_count == 3

def test_gives_up_after_max_retries():
    """Test that service stops retrying after max attempts."""
    client = Mock()
    client.request.side_effect = ConnectionError("Failed")

    service = ServiceWithRetry(client, max_retries=3)

    with pytest.raises(ConnectionError):
        service.fetch()

    assert client.request.call_count == 3

def test_does_not_retry_on_permanent_error():
    """Test that permanent errors are not retried."""
    client = Mock()
    client.request.side_effect = ValueError("Invalid input")

    service = ServiceWithRetry(client, max_retries=3)

    with pytest.raises(ValueError):
        service.fetch()

    # Only called once - no retry for ValueError
    assert client.request.call_count == 1

Mocking Time with Freezegun

Use freezegun to control time in tests for predictable time-dependent behavior.

from freezegun import freeze_time
from datetime import datetime, timedelta

@freeze_time("2026-01-15 10:00:00")
def test_token_expiry():
    """Test token expires at correct time."""
    token = create_token(expires_in_seconds=3600)
    assert token.expires_at == datetime(2026, 1, 15, 11, 0, 0)

@freeze_time("2026-01-15 10:00:00")
def test_is_expired_returns_false_before_expiry():
    """Test token is not expired when within validity period."""
    token = create_token(expires_in_seconds=3600)
    assert not token.is_expired()

@freeze_time("2026-01-15 12:00:00")
def test_is_expired_returns_true_after_expiry():
    """Test token is expired after validity period."""
    token = Token(expires_at=datetime(2026, 1, 15, 11, 30, 0))
    assert token.is_expired()

def test_with_time_travel():
    """Test behavior across time using freeze_time context."""
    with freeze_time("2026-01-01") as frozen_time:
        item = create_item()
        assert item.created_at == datetime(2026, 1, 1)

        # Move forward in time
        frozen_time.move_to("2026-01-15")
        assert item.age_days == 14

Test Markers

# test_markers.py
import pytest

@pytest.mark.slow
def test_slow_operation():
    """Mark slow tests."""
    import time
    time.sleep(2)


@pytest.mark.integration
def test_database_integration():
    """Mark integration tests."""
    pass


@pytest.mark.skip(reason="Feature not implemented yet")
def test_future_feature():
    """Skip tests temporarily."""
    pass


@pytest.mark.skipif(os.name == "nt", reason="Unix only test")
def test_unix_specific():
    """Conditional skip."""
    pass


@pytest.mark.xfail(reason="Known bug #123")
def test_known_bug():
    """Mark expected failures."""
    assert False


# Run with:
# pytest -m slow          # Run only slow tests
# pytest -m "not slow"    # Skip slow tests
# pytest -m integration   # Run integration tests

Coverage Reporting

# Install coverage
pip install pytest-cov

# Run tests with coverage
pytest --cov=myapp tests/

# Generate HTML report
pytest --cov=myapp --cov-report=html tests/

# Fail if coverage below threshold
pytest --cov=myapp --cov-fail-under=80 tests/

# Show missing lines
pytest --cov=myapp --cov-report=term-missing tests/

For advanced patterns (async testing, monkeypatching, property-based testing, database testing, CI/CD integration, and configuration), see references/advanced-patterns.md

能力边界

✅ 擅长处理

• 全栈开发相关的最佳实践
• 代码架构设计指导
• 性能优化建议

⚠️ 需要素材

• 具体的项目上下文
• 技术栈信息

❌ 超出范围

• 非技术领域的业务决策
• 具体的代码实现（仅提供指导）

Gotchas

1. 过度设计

避免在简单场景中使用复杂的设计模式

2. 性能忽视

不要忽视性能优化，特别是在生产环境中

3. 安全考虑

始终考虑安全性和数据保护

4. 测试覆盖

确保有足够的测试覆盖，特别是边界情况

5. 文档维护

保持文档与代码同步更新