1.15Writing CRUD Tests

# test_users.py

def test_create_user(api, create_user):
    """Verify user creation returns correct data."""
    user = create_user(name="Alice", role="admin")
    assert user["name"] == "Alice"
    assert user["role"] == "admin"
    assert "id" in user


def test_get_user(api, create_user):
    """Verify fetching a user by ID returns correct data."""
    user = create_user(name="Bob")
    r = api.get(f"/users/{user['id']}")
    assert r.status_code == 200
    assert r.json()["name"] == "Bob"


def test_update_user(api, create_user):
    """Verify user update modifies the correct fields."""
    user = create_user(name="Original")
    r = api.put(f"/users/{user['id']}", json={
        "name": "Updated",
        "email": user["email"],
        "role": user["role"]
    })
    assert r.status_code == 200
    assert r.json()["name"] == "Updated"


def test_delete_user(api, create_user):
    """Verify user deletion and subsequent 404."""
    user = create_user()
    r = api.delete(f"/users/{user['id']}")
    assert r.status_code == 204
    # Verify deletion
    r = api.get(f"/users/{user['id']}")
    assert r.status_code == 404


@pytest.mark.parametrize("email,expected", [
    ("valid@test.com", 201),
    ("", 400),
    ("not-an-email", 422),
    ("a" * 300 + "@test.com", 422),
])
def test_create_user_email_validation(api, email, expected):
    """Verify email validation across various inputs."""
    r = api.post("/users", json={
        "name": "Test", "email": email, "role": "viewer"
    })
    assert r.status_code == expected

COMMON MISTAKE: Writing tests that depend on data created by other tests. Each test should create its own data and clean up after itself. Use factory fixtures to ensure test independence.

2.3 Newman vs pytest: When to Use Which

Aspect	Newman	pytest + requests
Learning curve	Low (if already using Postman)	Medium (requires Python)
Flexibility	Limited to Postman scripting	Full Python ecosystem
Data-driven testing	CSV/JSON data files	pytest parametrize, fixtures
Maintenance at scale	Collections become unwieldy	Standard code with modules and imports
Debugging	Postman console	Python debugger, logging
Reusable utilities	Limited	Full Python: custom assertions, helpers, libraries
Version control	JSON exports (hard to diff)	Python files (easy to diff and review)
Team collaboration	Postman workspaces	Git (standard code review workflow)