Python Testing Best Practices
Core Philosophy
Write invariant-based tests that verify what SHOULD be true, not bug-affirming tests that prove bugs existed.
Test-Driven Development (TDD)
ALWAYS use TDD when fixing bugs:
- •Find existing tests for the broken functionality
- •Run them to verify they pass (shouldn't catch bug)
- •Improve tests until they fail (exposing the bug)
- •Fix the code to make tests pass
- •Verify all tests pass
Quick Start
bash
# Run tests pytest tests/path/to/test.py -v # Run with coverage pytest --cov=src --cov-report=term-missing
Critical Rules
Mocking - ALWAYS use patch.object
python
# CORRECT - refactor-safe
from unittest.mock import patch
@patch.object(MyClass, 'method_name')
def test_with_mock(mock_method):
...
# WRONG - breaks silently on refactor
@patch('module.path.MyClass.method_name')
def test_with_mock(mock_method):
...
Mock Dependencies, NOT the System Under Test
python
# CORRECT - Mock dependencies, test real code generator = NewsPostGenerator() generator._queries_chain = AsyncMock() # Dependency - mock it generator._search_engine = AsyncMock() # Dependency - mock it await generator.generate_news_post(...) # SUT - actually runs # WRONG - Tests nothing generator = AsyncMock(spec=NewsPostGenerator)
Test Data - ALWAYS use Pydantic models
python
# CORRECT - validation, type safety
def create_test_result(channel_id: str) -> VideoModerationResult:
return VideoModerationResult(
channel_id=channel_id,
user_id="test_user",
timestamp=datetime.now(UTC),
details=VideoModerationDetails(is_appropriate=True)
)
# WRONG - no validation, won't catch schema changes
def create_test_data():
return {"channel_id": "test", "user_id": "user123"}
Constants - NEVER use naked literals
python
# CORRECT - relationships explicit
DEFAULT_RECHECK_INTERVAL = 60
STALE_AGE = DEFAULT_RECHECK_INTERVAL + MODERATION_DURATION + 10
def test_staleness_detection():
timestamp = datetime.now(UTC) - timedelta(seconds=STALE_AGE)
assert is_stale(result)
# WRONG - magic numbers
def test_staleness_detection():
timestamp = datetime.now(UTC) - timedelta(seconds=120) # Why 120?
Invariant Testing
python
# CORRECT - Test what SHOULD be true
def test_drip_selector_populated_with_all_drip_names():
"""INVARIANT: Selector contains all drip names from config."""
config = make_config_with_drips(["drip1", "drip2", "drip3"])
page = setup_page_with_config(config)
assert page.drip_selector.options == ["drip1", "drip2", "drip3"]
# WRONG - Bug-affirming test
def test_bug_123_drip_selector_empty():
"""Bug 123: Drip selector is empty."""
assert len(drip_selector.options) > 0 # Proves bug, doesn't verify correctness
E2E Testing - Call Production Code
python
# CORRECT - Call actual production code
async def test_news_with_image_e2e():
await news_flow.create_news_post(flow_input)
published_event = mock_kafka.publish_object.call_args.kwargs["output"]
assert published_event.attachments is not None # Fails if code forgot attachment
# WRONG - Manually construct state
async def test_news_with_image_e2e():
activity_event = FeedActivityEvent(
attachments=[news_post.attachment], # WE added this, not production code!
)
# Test passes even if production code is broken
Access Mock Args Explicitly
python
# CORRECT - Clear and explicit flow_input = call_args.args[0] delay = call_args.kwargs["delay"] # WRONG - Cryptic flow_input = call_args[0][0] delay = call_args[1]["delay"]
Testing Checklist
Before committing:
- • All imports at top of file
- • Using
patch.object, notpatch - • Mocking dependencies, not SUT
- • No mocking of model classes
- • Test data uses Pydantic models
- • No naked literals - all values are constants
- • Mock args accessed with
.args[N]/.kwargs["name"] - • E2E tests call actual production code
- • Tests verify invariants, not just "no error"
- • 100% coverage of new code
Reference Files
For detailed patterns and examples:
- •references/mocking.md - Mocking strategies and when to mock
- •references/test-data.md - Test data creation patterns
- •references/e2e-testing.md - E2E and user journey patterns
- •references/testability.md - Extracting testable code
Related Skills
- •For debugging bugs, see
/pythonista-debugging - •For type safety, see
/pythonista-typing - •For code review, see
/pythonista-reviewing