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Unit Testing Best Practices Cheat Sheet

Unit Testing Best Practices Cheat Sheet

The Arrange-Act-Assert pattern, mocking external dependencies, parametrized tests, and the FIRST principles for writing reliable unit tests.

1 PageBeginnerApr 2, 2026

Arrange-Act-Assert

Structuring a pytest test into setup, action, and verification.

python
import pytestdef divide(a, b):    if b == 0:        raise ValueError("cannot divide by zero")    return a / bdef test_divide_returns_quotient():    # Arrange    a, b = 10, 2    # Act    result = divide(a, b)    # Assert    assert result == 5def test_divide_by_zero_raises():    with pytest.raises(ValueError, match="cannot divide by zero"):        divide(10, 0)

Mocking Dependencies

Isolating the unit under test with unittest.mock.

python
from unittest.mock import Mock, patchclass EmailService:    def send(self, to, body):        ...  # calls a real SMTP serverdef notify_user(email_service, user_email):    email_service.send(user_email, "Welcome!")    return Truedef test_notify_user_calls_send():    mock_service = Mock(spec=EmailService)    notify_user(mock_service, "[email protected]")    mock_service.send.assert_called_once_with("[email protected]", "Welcome!")@patch("mymodule.requests.get")   # patch where it's used, not where it's defineddef test_fetch_uses_requests_get(mock_get):    mock_get.return_value.status_code = 200    mock_get.return_value.json.return_value = {"ok": True}

Parametrized Tests

Running the same test logic against multiple input/output pairs.

python
import pytest@pytest.mark.parametrize("a, b, expected", [    (2, 3, 5),    (-1, 1, 0),    (0, 0, 0),])def test_add(a, b, expected):    assert a + b == expected

Testing Best Practices

Habits that keep a test suite fast, trustworthy, and maintainable.

  • FIRST principles- Tests should be Fast, Independent, Repeatable, Self-validating, and Timely
  • Descriptive names- Name tests after the behavior and expectation, e.g. test_returns_404_when_user_not_found
  • One behavior per test- Each test should verify a single behavior so failures pinpoint the exact problem
  • Avoid testing internals- Assert on observable outputs/behavior, not private implementation details, so refactors don't break tests
  • Mock external dependencies- Isolate the unit under test from databases, network calls, and the filesystem with test doubles
  • Use fixtures for setup- Share reusable setup/teardown code (e.g. pytest fixtures) instead of duplicating it in every test
  • Cover edge cases- Test empty inputs, boundary values, nulls, and error paths, not just the happy path
  • Keep tests deterministic- Avoid depending on real time, random values, network access, or execution order between tests

Test Doubles: Dummy, Stub, Spy, Mock, Fake

Distinguishing the five kinds of test double and when each one is the right tool.

python
from unittest.mock import Mockclass RealPaymentGateway:    def charge(self, amount):        ...  # hits a real network endpoint# Dummy: passed in but never actually used, just satisfies a signaturedummy_logger = None# Stub: returns canned answers, no behavior verificationclass StubGateway:    def charge(self, amount):        return {"status": "approved"}# Spy: records how it was called so the test can assert on that afterwardclass SpyGateway:    def __init__(self):        self.calls = []    def charge(self, amount):        self.calls.append(amount)        return {"status": "approved"}# Mock: pre-programmed expectations, fails the test if they go unmetmock_gateway = Mock(spec=RealPaymentGateway)mock_gateway.charge.return_value = {"status": "approved"}# Fake: a working, lightweight implementation (e.g. in-memory store) used instead of the real oneclass FakeGateway:    def __init__(self):        self.ledger = {}    def charge(self, amount):        self.ledger["last"] = amount        return {"status": "approved"}

Property-Based Testing with Hypothesis

Asserting invariants that must hold for all inputs instead of hand-picking individual example cases.

python
from hypothesis import given, strategies as stdef reverse(lst):    return lst[::-1]# Hypothesis generates hundreds of inputs and shrinks failures down to a# minimal reproducing example automatically@given(st.lists(st.integers()))def test_reverse_twice_is_identity(lst):    assert reverse(reverse(lst)) == lst@given(st.lists(st.integers(), min_size=1))def test_reverse_preserves_length_and_elements(lst):    reversed_lst = reverse(lst)    assert len(reversed_lst) == len(lst)    assert sorted(reversed_lst) == sorted(lst)

Testing Async Code

Awaiting coroutines under test directly with pytest-asyncio instead of blocking on an event loop manually.

python
import pytestimport asyncioasync def fetch_user(user_id, db):    return await db.get(user_id)@pytest.mark.asyncioasync def test_fetch_user_returns_record():    class FakeDb:        async def get(self, user_id):            await asyncio.sleep(0)  # simulate an await point            return {"id": user_id, "name": "Ada"}    result = await fetch_user(1, FakeDb())    assert result == {"id": 1, "name": "Ada"}# pyproject.toml:# [tool.pytest.ini_options]# asyncio_mode = "auto"   # lets async def tests run without the marker

Fixture Scopes & Dependency Injection

Controlling how often a pytest fixture is rebuilt with scope, and injecting fresh state per test.

python
import pytestclass Database:    def __init__(self):        self.records = []    def insert(self, item):        self.records.append(item)@pytest.fixture(scope="function")   # fresh instance per test (the default)def db():    return Database()@pytest.fixture(scope="session")    # built once, shared across the whole rundef expensive_config():    return {"timeout": 30, "retries": 3}def test_insert_adds_record(db):    db.insert("a")    assert db.records == ["a"]def test_insert_is_isolated(db):    # db is a brand-new instance here -- no leakage from the previous test    assert db.records == []

Advanced Testing Vocabulary

Concepts that separate a merely-passing test suite from one you can actually trust.

  • Test Pyramid- A large base of fast unit tests, fewer integration tests, and a thin layer of slow end-to-end tests
  • Mutation Testing- Deliberately injecting small code bugs (mutants) and checking whether the suite fails -- surfaces weak assertions coverage alone misses
  • Flaky Test- A test that passes and fails intermittently without code changes, usually from timing, ordering, or shared state
  • Snapshot/Golden Testing- Comparing output against a previously approved reference file, useful for large structured outputs
  • Contract Testing- Verifying that a consumer and provider of an API agree on the same interface without standing up both services
  • Coverage vs Mutation Coverage- Line/branch coverage shows what code ran; mutation coverage shows what was actually verified by an assertion
  • Test Isolation- Each test sets up and tears down its own state so tests can run in any order or in parallel
  • Golden Master Testing- Capturing legacy system output as a baseline before refactoring, to detect unintended behavior changes
Pro Tip

If you find yourself mocking three or four collaborators just to test one function, treat that as a signal the function is doing too much — refactor it before writing more tests around it.

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