Python Testing Patterns logo

Python Testing Patterns

Community
jh941213
python-testing-patterns

Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. 트리거: "pytest", "Python 테스트", "fixture", "mocking", "테스트 작성" 안티-트리거: "JavaScript 테스트", "Jest", "E2E 테스트"

Overview

Publisherjh941213
Repositorymy-cc-harness
Skill namepython-testing-patterns
Stars
125
Forks
35
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by jh941213 on GitHub. Read the source before you install it.

Installation

Install the Python Testing Patterns AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jh941213/my-cc-harness.git /tmp/my-cc-harness
mkdir -p .claude/skills
cp -r /tmp/my-cc-harness/skills/python-testing-patterns .claude/skills/python-testing-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Python Testing Patterns in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Python Testing Patterns on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Python Testing Patterns is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

python
# 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

Fundamental Patterns

Pattern 1: Basic pytest Tests

python
# test_calculator.py
import pytest

class Calculator:
    """Simple calculator for testing."""

    def add(self, a: float, b: float) -> float:
        return a + b

    def subtract(self, a: float, b: float) -> float:
        return a - b

    def multiply(self, a: float, b: float) -> float:
        return a * b

    def divide(self, a: float, b: float) -> float:
        if b == 0:
            raise ValueError("Cannot divide by zero")
        return a / b


def test_addition():
    """Test addition."""
    calc = Calculator()
    assert calc.add(2, 3) == 5
    assert calc.add(-1, 1) == 0
    assert calc.add(0, 0) == 0


def test_subtraction():
    """Test subtraction."""
    calc = Calculator()
    assert calc.subtract(5, 3) == 2
    assert calc.subtract(0, 5) == -5


def test_multiplication():
    """Test multiplication."""
    calc = Calculator()
    assert calc.multiply(3, 4) == 12
    assert calc.multiply(0, 5) == 0


def test_division():
    """Test division."""
    calc = Calculator()
    assert calc.divide(6, 3) == 2
    assert calc.divide(5, 2) == 2.5


def test_division_by_zero():
    """Test division by zero raises error."""
    calc = Calculator()
    with pytest.raises(ValueError, match="Cannot divide by zero"):
        calc.divide(5, 0)

Pattern 2: Fixtures for Setup and Teardown

python
# test_database.py
import pytest
from typing import Generator

class Database:
    """Simple database class."""

    def __init__(self, connection_string: str):
        self.connection_string = connection_string
        self.connected = False

    def connect(self):
        """Connect to database."""
        self.connected = True

    def disconnect(self):
        """Disconnect from database."""
        self.connected = False

    def query(self, sql: str) -> list:
        """Execute query."""
        if not self.connected:
            raise RuntimeError("Not connected")
        return [{"id": 1, "name": "Test"}]


@pytest.fixture
def db() -> Generator[Database, None, None]:
    """Fixture that provides connected database."""
    # Setup
    database = Database("sqlite:///:memory:")
    database.connect()

    # Provide to test
    yield database

    # Teardown
    database.disconnect()


def test_database_query(db):
    """Test database query with fixture."""
    results = db.query("SELECT * FROM users")
    assert len(results) == 1
    assert results[0]["name"] == "Test"


@pytest.fixture(scope="session")
def app_config():
    """Session-scoped fixture - created once per test session."""
    return {
        "database_url": "postgresql://localhost/test",
        "api_key": "test-key",
        "debug": True
    }


@pytest.fixture(scope="module")
def api_client(app_config):
    """Module-scoped fixture - created once per test module."""
    # Setup expensive resource
    client = {"config": app_config, "session": "active"}
    yield client
    # Cleanup
    client["session"] = "closed"


def test_api_client(api_client):
    """Test using api client fixture."""
    assert api_client["session"] == "active"
    assert api_client["config"]["debug"] is True

Pattern 3: Parameterized Tests

python
# test_validation.py
import pytest

def is_valid_email(email: str) -> bool:
    """Check if email is valid."""
    return "@" in email and "." in email.split("@")[1]


@pytest.mark.parametrize("email,expected", [
    ("user@example.com", True),
    ("test.user@domain.co.uk", True),
    ("invalid.email", False),
    ("@example.com", False),
    ("user@domain", False),
    ("", False),
])
def test_email_validation(email, expected):
    """Test email validation with various inputs."""
    assert is_valid_email(email) == expected


@pytest.mark.parametrize("a,b,expected", [
    (2, 3, 5),
    (0, 0, 0),
    (-1, 1, 0),
    (100, 200, 300),
    (-5, -5, -10),
])
def test_addition_parameterized(a, b, expected):
    """Test addition with multiple parameter sets."""
    from test_calculator import Calculator
    calc = Calculator()
    assert calc.add(a, b) == expected


# Using pytest.param for special cases
@pytest.mark.parametrize("value,expected", [
    pytest.param(1, True, id="positive"),
    pytest.param(0, False, id="zero"),
    pytest.param(-1, False, id="negative"),
])
def test_is_positive(value, expected):
    """Test with custom test IDs."""
    assert (value > 0) == expected

Pattern 4: Mocking with unittest.mock

python
# test_api_client.py
import pytest
from unittest.mock import Mock, patch, MagicMock
import requests

class APIClient:
    """Simple API client."""

    def __init__(self, base_url: str):
        self.base_url = base_url

    def get_user(self, user_id: int) -> dict:
        """Fetch user from API."""
        response = requests.get(f"{self.base_url}/users/{user_id}")
        response.raise_for_status()
        return response.json()

    def create_user(self, data: dict) -> dict:
        """Create new user."""
        response = requests.post(f"{self.base_url}/users", json=data)
        response.raise_for_status()
        return response.json()


def test_get_user_success():
    """Test successful API call with mock."""
    client = APIClient("https://api.example.com")

    mock_response = Mock()
    mock_response.json.return_value = {"id": 1, "name": "John Doe"}
    mock_response.raise_for_status.return_value = None

    with patch("requests.get", return_value=mock_response) as mock_get:
        user = client.get_user(1)

        assert user["id"] == 1
        assert user["name"] == "John Doe"
        mock_get.assert_called_once_with("https://api.example.com/users/1")


def test_get_user_not_found():
    """Test API call with 404 error."""
    client = APIClient("https://api.example.com")

    mock_response = Mock()
    mock_response.raise_for_status.side_effect = requests.HTTPError("404 Not Found")

    with patch("requests.get", return_value=mock_response):
        with pytest.raises(requests.HTTPError):
            client.get_user(999)


@patch("requests.post")
def test_create_user(mock_post):
    """Test user creation with decorator syntax."""
    client = APIClient("https://api.example.com")

    mock_post.return_value.json.return_value = {"id": 2, "name": "Jane Doe"}
    mock_post.return_value.raise_for_status.return_value = None

    user_data = {"name": "Jane Doe", "email": "jane@example.com"}
    result = client.create_user(user_data)

    assert result["id"] == 2
    mock_post.assert_called_once()
    call_args = mock_post.call_args
    assert call_args.kwargs["json"] == user_data

Pattern 5: Testing Exceptions

python
# test_exceptions.py
import pytest

def divide(a: float, b: float) -> float:
    """Divide a by b."""
    if b == 0:
        raise ZeroDivisionError("Division by zero")
    if not isinstance(a, (int, float)) or not isinstance(b, (int, float)):
        raise TypeError("Arguments must be numbers")
    return a / b


def test_zero_division():
    """Test exception is raised for division by zero."""
    with pytest.raises(ZeroDivisionError):
        divide(10, 0)


def test_zero_division_with_message():
    """Test exception message."""
    with pytest.raises(ZeroDivisionError, match="Division by zero"):
        divide(5, 0)


def test_type_error():
    """Test type error exception."""
    with pytest.raises(TypeError, match="must be numbers"):
        divide("10", 5)


def test_exception_info():
    """Test accessing exception info."""
    with pytest.raises(ValueError) as exc_info:
        int("not a number")

    assert "invalid literal" in str(exc_info.value)

Advanced Patterns

Pattern 6: Testing Async Code

python
# test_async.py
import pytest
import asyncio

async def fetch_data(url: str) -> dict:
    """Fetch data asynchronously."""
    await asyncio.sleep(0.1)
    return {"url": url, "data": "result"}


@pytest.mark.asyncio
async def test_fetch_data():
    """Test async function."""
    result = await fetch_data("https://api.example.com")
    assert result["url"] == "https://api.example.com"
    assert "data" in result


@pytest.mark.asyncio
async def test_concurrent_fetches():
    """Test concurrent async operations."""
    urls = ["url1", "url2", "url3"]
    tasks = [fetch_data(url) for url in urls]
    results = await asyncio.gather(*tasks)

    assert len(results) == 3
    assert all("data" in r for r in results)


@pytest.fixture
async def async_client():
    """Async fixture."""
    client = {"connected": True}
    yield client
    client["connected"] = False


@pytest.mark.asyncio
async def test_with_async_fixture(async_client):
    """Test using async fixture."""
    assert async_client["connected"] is True

Pattern 7: Monkeypatch for Testing

python
# test_environment.py
import os
import pytest

def get_database_url() -> str:
    """Get database URL from environment."""
    return os.environ.get("DATABASE_URL", "sqlite:///:memory:")


def test_database_url_default():
    """Test default database URL."""
    # Will use actual environment variable if set
    url = get_database_url()
    assert url


def test_database_url_custom(monkeypatch):
    """Test custom database URL with monkeypatch."""
    monkeypatch.setenv("DATABASE_URL", "postgresql://localhost/test")
    assert get_database_url() == "postgresql://localhost/test"


def test_database_url_not_set(monkeypatch):
    """Test when env var is not set."""
    monkeypatch.delenv("DATABASE_URL", raising=False)
    assert get_database_url() == "sqlite:///:memory:"


class Config:
    """Configuration class."""

    def __init__(self):
        self.api_key = "production-key"

    def get_api_key(self):
        return self.api_key


def test_monkeypatch_attribute(monkeypatch):
    """Test monkeypatching object attributes."""
    config = Config()
    monkeypatch.setattr(config, "api_key", "test-key")
    assert config.get_api_key() == "test-key"

Pattern 8: Temporary Files and Directories

python
# test_file_operations.py
import pytest
from pathlib import Path

def save_data(filepath: Path, data: str):
    """Save data to file."""
    filepath.write_text(data)


def load_data(filepath: Path) -> str:
    """Load data from file."""
    return filepath.read_text()


def test_file_operations(tmp_path):
    """Test file operations with temporary directory."""
    # tmp_path is a pathlib.Path object
    test_file = tmp_path / "test_data.txt"

    # Save data
    save_data(test_file, "Hello, World!")

    # Verify file exists
    assert test_file.exists()

    # Load and verify data
    data = load_data(test_file)
    assert data == "Hello, World!"


def test_multiple_files(tmp_path):
    """Test with multiple temporary files."""
    files = {
        "file1.txt": "Content 1",
        "file2.txt": "Content 2",
        "file3.txt": "Content 3"
    }

    for filename, content in files.items():
        filepath = tmp_path / filename
        save_data(filepath, content)

    # Verify all files created
    assert len(list(tmp_path.iterdir())) == 3

    # Verify contents
    for filename, expected_content in files.items():
        filepath = tmp_path / filename
        assert load_data(filepath) == expected_content

Pattern 9: Custom Fixtures and Conftest

python
# conftest.py
"""Shared fixtures for all tests."""
import pytest

@pytest.fixture(scope="session")
def database_url():
    """Provide database URL for all tests."""
    return "postgresql://localhost/test_db"


@pytest.fixture(autouse=True)
def reset_database(database_url):
    """Auto-use fixture that runs before each test."""
    # Setup: Clear database
    print(f"Clearing database: {database_url}")
    yield
    # Teardown: Clean up
    print("Test completed")


@pytest.fixture
def sample_user():
    """Provide sample user data."""
    return {
        "id": 1,
        "name": "Test User",
        "email": "test@example.com"
    }


@pytest.fixture
def sample_users():
    """Provide list of sample users."""
    return [
        {"id": 1, "name": "User 1"},
        {"id": 2, "name": "User 2"},
        {"id": 3, "name": "User 3"},
    ]


# Parametrized fixture
@pytest.fixture(params=["sqlite", "postgresql", "mysql"])
def db_backend(request):
    """Fixture that runs tests with different database backends."""
    return request.param


def test_with_db_backend(db_backend):
    """This test will run 3 times with different backends."""
    print(f"Testing with {db_backend}")
    assert db_backend in ["sqlite", "postgresql", "mysql"]

Pattern 10: Property-Based Testing

python
# test_properties.py
from hypothesis import given, strategies as st
import pytest

def reverse_string(s: str) -> str:
    """Reverse a string."""
    return s[::-1]


@given(st.text())
def test_reverse_twice_is_original(s):
    """Property: reversing twice returns original."""
    assert reverse_string(reverse_string(s)) == s


@given(st.text())
def test_reverse_length(s):
    """Property: reversed string has same length."""
    assert len(reverse_string(s)) == len(s)


@given(st.integers(), st.integers())
def test_addition_commutative(a, b):
    """Property: addition is commutative."""
    assert a + b == b + a


@given(st.lists(st.integers()))
def test_sorted_list_properties(lst):
    """Property: sorted list is ordered."""
    sorted_lst = sorted(lst)

    # Same length
    assert len(sorted_lst) == len(lst)

    # All elements present
    assert set(sorted_lst) == set(lst)

    # Is ordered
    for i in range(len(sorted_lst) - 1):
        assert sorted_lst[i] <= sorted_lst[i + 1]

Testing Best Practices

Test Organization

python
# 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

python
# Good test names
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
def test_1():  # Not descriptive
    pass


def test_user():  # Too vague
    pass


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

Test Markers

python
# 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

bash
# 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/

Testing Database Code

python
# test_database_models.py
import pytest
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, Session

Base = declarative_base()


class User(Base):
    """User model."""
    __tablename__ = "users"

    id = Column(Integer, primary_key=True)
    name = Column(String(50))
    email = Column(String(100), unique=True)


@pytest.fixture(scope="function")
def db_session() -> Session:
    """Create in-memory database for testing."""
    engine = create_engine("sqlite:///:memory:")
    Base.metadata.create_all(engine)

    SessionLocal = sessionmaker(bind=engine)
    session = SessionLocal()

    yield session

    session.close()


def test_create_user(db_session):
    """Test creating a user."""
    user = User(name="Test User", email="test@example.com")
    db_session.add(user)
    db_session.commit()

    assert user.id is not None
    assert user.name == "Test User"


def test_query_user(db_session):
    """Test querying users."""
    user1 = User(name="User 1", email="user1@example.com")
    user2 = User(name="User 2", email="user2@example.com")

    db_session.add_all([user1, user2])
    db_session.commit()

    users = db_session.query(User).all()
    assert len(users) == 2


def test_unique_email_constraint(db_session):
    """Test unique email constraint."""
    from sqlalchemy.exc import IntegrityError

    user1 = User(name="User 1", email="same@example.com")
    user2 = User(name="User 2", email="same@example.com")

    db_session.add(user1)
    db_session.commit()

    db_session.add(user2)

    with pytest.raises(IntegrityError):
        db_session.commit()

CI/CD Integration

yaml
# .github/workflows/test.yml
name: Tests

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest

    strategy:
      matrix:
        python-version: ["3.9", "3.10", "3.11", "3.12"]

    steps:
      - uses: actions/checkout@v3

      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: ${{ matrix.python-version }}

      - name: Install dependencies
        run: |
          pip install -e ".[dev]"
          pip install pytest pytest-cov

      - name: Run tests
        run: |
          pytest --cov=myapp --cov-report=xml

      - name: Upload coverage
        uses: codecov/codecov-action@v3
        with:
          file: ./coverage.xml

Configuration Files

ini
# pytest.ini
[pytest]
testpaths = tests
python_files = test_*.py
python_classes = Test*
python_functions = test_*
addopts =
    -v
    --strict-markers
    --tb=short
    --cov=myapp
    --cov-report=term-missing
markers =
    slow: marks tests as slow
    integration: marks integration tests
    unit: marks unit tests
    e2e: marks end-to-end tests
toml
# pyproject.toml
[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = ["test_*.py"]
addopts = [
    "-v",
    "--cov=myapp",
    "--cov-report=term-missing",
]

[tool.coverage.run]
source = ["myapp"]
omit = ["*/tests/*", "*/migrations/*"]

[tool.coverage.report]
exclude_lines = [
    "pragma: no cover",
    "def __repr__",
    "raise AssertionError",
    "raise NotImplementedError",
]

Resources

Best Practices Summary

  1. Write tests first (TDD) or alongside code
  2. One assertion per test when possible
  3. Use descriptive test names that explain behavior
  4. Keep tests independent and isolated
  5. Use fixtures for setup and teardown
  6. Mock external dependencies appropriately
  7. Parametrize tests to reduce duplication
  8. Test edge cases and error conditions
  9. Measure coverage but focus on quality
  10. Run tests in CI/CD on every commit

Frequently asked questions

What does the Python Testing Patterns AI skill do?

Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. 트리거: "pytest", "Python 테스트", "fixture", "mocking", "테스트 작성" 안티-트리거: "JavaScript 테스트", "Jest", "E2E 테스트"

Why use Python Testing Patterns on TypingMind?

Because you install it once and use it with any model. Python Testing Patterns is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Python Testing Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jh941213/my-cc-harness/tree/main/skills/python-testing-patterns. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Python Testing Patterns?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Python Testing Patterns?

As many as you like. As long as a model supports skills, you can use Python Testing Patterns with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Python Testing Patterns AI skill free?

It is published on GitHub by jh941213. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇