Python Testing Patterns
Seth HobsonCommunityCreated by Seth Hobson
- Skill
- Guidelines
- Software development
- MIT
- Low risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured approach to testing Python applications. It covers essential testing concepts including the Arrange-Act-Assert pattern, test isolation, and coverage analysis. The content includes practical examples for unit, integration, and functional testing, alongside advanced techniques such as mocking, parameterization, async testing, and environment manipulation using pytest.
What it does
- Explains core testing concepts like test types and the AAA structure.
- Provides patterns for organizing test suites and naming conventions.
- Demonstrates how to use fixtures for setup and teardown operations.
- Shows how to mock dependencies and control time in tests.
- Covers advanced testing scenarios like async code and environment variables.
- Details how to generate coverage reports and use test markers.
When to use it
- When writing unit or integration tests for Python code.
- When setting up test infrastructure or configuring test suites.
- When implementing test-driven development practices.
- When needing to mock external services or handle time-dependent logic.
- When debugging failing tests or improving test coverage.
Advantages
- Promotes consistent test organization and naming.
- Provides reusable patterns for common testing challenges.
- Includes clear examples for complex scenarios like async and environment mocking.
- Helps maintain test independence and clean state management.
Usage examples
- “How do I structure my pytest tests using the AAA pattern?”
- The agent explains the Arrange, Act, and Assert phases and provides a code example demonstrating how to set up data, execute the function, and verify the result.
- “Show me how to mock a service that has retry logic.”
- The agent provides a code example using unittest.mock to simulate transient failures and verify that the service retries the expected number of times.
- “How can I test code that depends on the current time?”
- The agent demonstrates the use of the freezegun library to freeze time, allowing for predictable assertions in time-dependent logic.
- “How do I run tests with coverage reporting?”
- The agent provides the necessary commands to install pytest-cov and run tests with coverage, including how to generate HTML reports and set thresholds.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/wshobson/python-testing-patterns/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip python-testing-patterns.zip -d .agents/skills
Documentation by Seth Hobson
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.pyDetailed 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.pyTest 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
passTesting 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 == 1Mocking 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 == 14Test 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 testsCoverage 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
Files
- ATTRIBUTION.mdAdded by AutoGineer1005 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md7.1 KiB
- references/advanced-patterns.md9.2 KiB
- references/details.md9.1 KiB
references/details.md
python-testing-patterns — detailed patterns and worked examples
Fundamental Patterns
Pattern 1: Basic pytest Tests
# 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
# 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 TruePattern 3: Parameterized Tests
# test_validation.py
import pytest
def is_valid_email(email: str) -> bool:
"""Check if email is valid."""
local, sep, domain = email.partition("@")
return bool(local) and bool(sep) and "." in domain and "@" not in domain
@pytest.mark.parametrize("email,expected", [
("[email protected]", True),
("[email protected]", 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) == expectedPattern 4: Mocking with unittest.mock
# 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": "[email protected]"}
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_dataPattern 5: Testing Exceptions
# 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)For advanced patterns including async testing, monkeypatching, temporary files, conftest setup, property-based testing, database testing, CI/CD integration, and configuration files, see references/advanced-patterns.md
Test Design Principles
One Behavior Per Test
Each test should verify exactly one behavior. This makes failures easy to diagnose and tests easy to maintain.
# BAD - testing multiple behaviors
def test_user_service():
user = service.create_user(data)
assert user.id is not None
assert user.email == data["email"]
updated = service.update_user(user.id, {"name": "New"})
assert updated.name == "New"
# GOOD - focused tests
def test_create_user_assigns_id():
user = service.create_user(data)
assert user.id is not None
def test_create_user_stores_email():
user = service.create_user(data)
assert user.email == data["email"]
def test_update_user_changes_name():
user = service.create_user(data)
updated = service.update_user(user.id, {"name": "New"})
assert updated.name == "New"Test Error Paths
Always test failure cases, not just happy paths.
def test_get_user_raises_not_found():
with pytest.raises(UserNotFoundError) as exc_info:
service.get_user("nonexistent-id")
assert "nonexistent-id" in str(exc_info.value)
def test_create_user_rejects_invalid_email():
with pytest.raises(ValueError, match="Invalid email format"):
service.create_user({"email": "not-an-email"})Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
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Attribution and licence
- Created by
- Seth Hobson
- Publisher
- Seth Hobson (Community)
- Source
- wshobson/agents · plugins/python-development/skills/python-testing-patterns · commit 4bde316
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Seth Hobson or Seth Hobson.
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