squid-testing-python

name	squid-testing-python
description	Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage.

Writing Effective Python Tests

Core Principles

Every test should be atomic, self-contained, and test single functionality. A test that tests multiple things is harder to debug and maintain.

Test Structure

Mirror the module layout

Keep a one-to-one relationship between test files and the modules they cover: myapp/service.py → tests/.../test_service.py. This makes the test for any given module obvious and keeps coverage gaps visible.

Follow AAA (Arrange, Act, Assert)

Structure each test body in three beats — set up inputs (Arrange), call the thing under test (Act), then assert on the result. Keep them in that order; don't interleave more setup after the act.

Atomic unit tests

Each test should verify a single behavior. The test name should tell you what's broken when it fails. Multiple assertions are fine when they all verify the same behavior.

# Good: Name tells you what's broken
def test_user_creation_sets_defaults():
    user = User(name="Alice")
    assert user.role == "member"
    assert user.id is not None
    assert user.created_at is not None

# Bad: If this fails, what behavior is broken?
def test_user():
    user = User(name="Alice")
    assert user.role == "member"
    user.promote()
    assert user.role == "admin"
    assert user.can_delete_others()

Use parameterization for variations of the same concept

import pytest

@pytest.mark.parametrize("input,expected", [
    ("hello", "HELLO"),
    ("World", "WORLD"),
    ("", ""),
    ("123", "123"),
])
def test_uppercase_conversion(input, expected):
    assert input.upper() == expected

Use separate tests for different functionality

Don't parameterize unrelated behaviors. If the test logic differs, write separate tests.

Project-Specific Rules

Imports at module level

Put ALL imports at the top of the file. Do not import inside test function bodies.

# Correct
import pytest
from myapp.service import do_work

def test_something():
    assert do_work() is not None

# Wrong - no local imports
def test_something():
    from myapp.service import do_work  # Don't do this
    ...

Async tests

If the project sets asyncio_mode = "auto" in pyproject.toml, write async tests without decorators:

# Correct (when asyncio_mode = "auto")
async def test_async_operation():
    result = await some_async_function()
    assert result == expected

Otherwise, mark explicitly with @pytest.mark.asyncio.

Inline snapshots for complex data

If the project uses inline-snapshot, use it for JSON schemas and complex structures:

from inline_snapshot import snapshot

def test_schema_generation():
    schema = generate_schema(MyModel)
    assert schema == snapshot()  # Will auto-populate on first run

Commands:

• pytest --inline-snapshot=create - populate empty snapshots
• pytest --inline-snapshot=fix - update after intentional changes

Fixtures

Share setup through fixtures, not setup/teardown methods

Put shared fixtures in conftest.py so they're available across test files without imports. Use fixtures (and their yield-based teardown) for setup and cleanup — avoid xUnit-style setUp/tearDown methods.

Prefer function-scoped fixtures

@pytest.fixture
def client():
    return Client()

def test_with_client(client):
    result = client.ping()
    assert result is not None

Use tmp_path for file operations

def test_file_writing(tmp_path):
    file = tmp_path / "test.txt"
    file.write_text("content")
    assert file.read_text() == "content"

Mocking

Mock at the boundary

Use pytest-mock's mocker fixture (preferred) or unittest.mock patches.

from unittest.mock import AsyncMock

async def test_external_api_call(mocker):
    mock = mocker.patch("mymodule.external_client.fetch", new_callable=AsyncMock)
    mock.return_value = {"data": "test"}
    result = await my_function()
    assert result == {"data": "test"}

Don't mock what you own

Test your code with real implementations when possible. Mock external services (HTTP APIs, third-party SDKs), not your own internal classes.

Don't write unit tests against infrastructure components

Orchestrators, model-serving runtimes, observability clients, and similar infrastructure should be exercised via integration tests, not unit tests with mocks of their internals.

Test Naming

Test files must be named test_*.py and test functions test_* so pytest discovers them. Beyond that, use descriptive names that explain the scenario:

# Good
def test_login_fails_with_invalid_password():
def test_user_can_update_own_profile():
def test_admin_can_delete_any_user():

# Bad
def test_login():
def test_update():
def test_delete():

Error Testing

import pytest

def test_raises_on_invalid_input():
    with pytest.raises(ValueError, match="must be positive"):
        calculate(-1)

async def test_async_raises():
    with pytest.raises(ConnectionError):
        await connect_to_invalid_host()

Running Tests

uv run pytest -n auto              # Run all tests in parallel
uv run pytest -n auto -x           # Stop on first failure
uv run pytest path/to/test.py      # Run specific file
uv run pytest -k "test_name"       # Run tests matching pattern
uv run pytest -m "not integration" # Exclude integration tests

Prefer project Make targets when available: make unit-tests, make integration-tests, make tests.

Checklist

Before submitting tests:

• Each test tests one thing
• Imports at module level
• Descriptive test names
• Async decorators consistent with project's asyncio mode
• Parameterization for variations of same behavior
• Separate tests for different behaviors
• No unit tests against infrastructure components (those go to integration tests)
• 0 warnings when running the test suite