Pytest avance : tester son code Python comme un professionnel

La plupart des suites de tests Python finissent par etre plus un fardeau qu’une aide. Voici les patterns qui construisent des tests durables.

Fixtures : le pattern le plus important

# conftest.py
import pytest

@pytest.fixture(scope="session")
def db_engine():
    engine = create_engine("sqlite:///:memory:")
    Base.metadata.create_all(engine)
    yield engine
    Base.metadata.drop_all(engine)

@pytest.fixture(scope="function")
def db_session(db_engine):
    connection = db_engine.connect()
    transaction = connection.begin()
    session = Session(bind=connection)
    yield session
    session.close()
    transaction.rollback()
    connection.close()

Parametriser les tests

@pytest.mark.parametrize("email,expected_valid", [
    ("alice@example.com", True),
    ("alice@", False),
    ("", False),
    ("alice@example.co.uk", True),
])
def test_email_validation(email, expected_valid):
    assert is_valid_email(email) == expected_valid

Un test, quatre cas couverts.

Mocker les dependances externes

from unittest.mock import patch

def test_send_notification_success(test_user):
    with patch('myapp.notifications.smtp_client') as mock_smtp:
        mock_smtp.send.return_value = True
        result = send_notification(test_user.email, "Bienvenue !")
        assert result is True
        mock_smtp.send.assert_called_once()

def test_send_notification_failure(test_user):
    with patch('myapp.notifications.smtp_client') as mock_smtp:
        mock_smtp.send.side_effect = SMTPException("Connection refused")
        with pytest.raises(NotificationError):
            send_notification(test_user.email, "Bienvenue !")

Notre formation Python couvre le testing avec pytest en profondeur.