python-testing-patterns

name	python-testing-patterns
description	Guides expert-level Python testing with pytest: fixture design, parametrize patterns, mocking strategies with unittest.mock, async test patterns, and test organization. Covers the testing pyramid for Python projects with emphasis on fast, reliable, maintainable tests. Use when the user asks about Python testing, pytest fixtures, test mocking, parametrized tests, async testing in Python, or organizing Python test suites. Do NOT use when the user asks about project setup (use `python-project-setup`), language-agnostic testing concepts (use `unit-testing-patterns`), or Python performance profiling (use `python-performance`).
license	Apache-2.0
metadata	{"author":"foundry-skills","version":"1.0.0","tags":"python testing tdd","category":"software-engineering","subcategory":"languages-runtimes","depends":"","disclaimer":"none","difficulty":"intermediate"}

Python Testing Patterns

When to Use

Use this skill when:

• User asks how to write tests in Python using pytest, including test discovery, fixture wiring, or assertion patterns
• User wants to design pytest fixtures for complex scenarios involving shared state, teardown, parameterization, or scoping
• User asks about @pytest.mark.parametrize for table-driven testing, including matrix combinations and conditional skipping
• User needs to mock dependencies using unittest.mock, including patch, MagicMock, AsyncMock, patch.dict, or patch.object
• User wants to test async Python code with pytest-asyncio, including async fixtures, async generators, and timeout behavior
• User asks about organizing test suites with conftest.py hierarchy, test markers, or coverage configuration
• User wants to apply the testing pyramid to a Python project: balancing unit, integration, and contract tests by speed and scope
• User asks about test doubles -- stubs, spies, fakes, and mocks -- and when to use each in a Python context
• User needs guidance on testing patterns specific to Python frameworks such as FastAPI, SQLAlchemy, Celery, or Pydantic

Do NOT use this skill when:

• User wants to set up a new Python project from scratch -- use python-project-setup instead
• User is asking about general testing philosophy or the testing pyramid as a concept -- use unit-testing-patterns instead
• User wants structured TDD workflow coaching (red-green-refactor cycles) -- use tdd-workflow instead
• User is asking about browser-based or end-to-end testing with Playwright or Selenium -- use e2e-testing-patterns instead
• User wants performance benchmarking or profiling of Python code -- use python-performance instead
• User is making high-level decisions about what to test and at what layer -- use test-strategy-design instead
• User is asking about CI/CD pipeline configuration for running tests -- use ci-pipeline-design instead

---

Process

1. Assess the Testing Context and Test Type

Before writing any code, classify what is being tested and choose the right test category. This determines every decision downstream.

• Pure functions with no I/O or side effects: Write direct unit tests with assert. No mocking needed. These are the fastest, most reliable tests in the suite. Push as many behaviors as possible into pure functions specifically to make them this easy to test.
• Classes with injected dependencies: Write unit tests using MagicMock or AsyncMock for collaborators. Verify behavior via return values and call assertions. Prefer constructor injection -- it makes the dependency boundary explicit and avoids patch entirely.
• Database interaction layers (repositories, DAOs): Write integration tests that talk to a real database (PostgreSQL in a Docker container or SQLite in-memory). Use transaction rollback per test to maintain isolation without schema recreation. These belong in a separate pytest mark (@pytest.mark.integration) and run in CI, not in pre-commit hooks.
• HTTP endpoints (FastAPI, Flask): Use framework-provided test clients (httpx.AsyncClient for FastAPI, flask.testing.FlaskClient for Flask). These are integration tests that exercise routing, serialization, and middleware, but mock outbound external calls.
• Async code: Check the event loop policy early. Confirm pytest-asyncio is installed and asyncio_mode = "auto" is set. All async test functions and async fixtures work without @pytest.mark.asyncio decoration in auto mode.
• Determine test speed targets: Unit tests should run in under 1ms each. A suite of 1000 unit tests should finish in under 2 seconds. Any test taking longer than 100ms is a candidate for mock replacement or integration test reclassification.

2. Design the Fixture Hierarchy

Fixtures are the backbone of a pytest suite. Bad fixture design causes slow, brittle, or non-isolated tests. Apply this decision framework precisely:

• Use scope="function" (default) for: any fixture that mutates state, any fixture wrapping a mock, any database transaction that rolls back, or anything that has side effects. This is the safe default -- prefer it unless you have a measured performance reason not to.
• Use scope="module" for: expensive read-only setup that is identical for every test in the file, such as parsing a large static test data file or compiling a regex that is expensive to build.
• Use scope="session" for: database engine creation, HTTP test client initialization, external service connections, and other one-time-per-run operations. Never store mutable test state in session-scoped fixtures.
• Use yield fixtures for all teardown: put setup before the yield and teardown after it. This ensures cleanup runs even when a test fails. Never use pytest.fixture with a return if the fixture allocates resources that need cleanup.
• Use autouse=True sparingly: Only for behavior that genuinely applies to every test in scope, such as resetting a global configuration registry or clearing a singleton cache. Always document why autouse is justified.
• Use params on fixtures to fan out test variants: @pytest.fixture(params=["sqlite", "postgres"], ids=["sqlite", "postgres"]) creates one test per parameter value for every test that uses that fixture. This is different from parametrize and is appropriate when the fixture itself is the variant (e.g., testing against multiple database backends).
• Stack conftest.py files hierarchically: tests/conftest.py provides project-wide fixtures. tests/unit/conftest.py provides unit-test-specific fixtures. tests/integration/conftest.py provides integration-test-specific fixtures. Never import fixtures across test files manually -- let pytest discover them.
• Avoid fixture overuse: A fixture that sets up 15 attributes and is used by 3 tests is a smell. Extract targeted fixtures that set up only what each test needs. Over-specified fixtures make tests fragile to unrelated changes.

3. Choose the Right Test Double Strategy

Python's unittest.mock offers multiple double types. Choosing the wrong one causes brittle tests or misses real bugs.

• MagicMock: Use for synchronous dependencies where you want automatic attribute access and call tracking. By default, every attribute access returns another MagicMock, which can mask typos -- always use spec=SomeClass to constrain the mock to the real interface: MagicMock(spec=UserRepository).
• AsyncMock: Use for any dependency whose methods are async def. Python 3.8+ provides this in unittest.mock. If you patch an async method with a regular MagicMock, the test will not await it correctly and will return a coroutine object instead of a value.
• patch as a decorator vs. context manager: Use @patch("myapp.services.user_service.EmailSender") when the mock scope is the entire test function. Use with patch(...) as mock: when the mock applies to only part of the test. Always patch at the point of use (where the name is imported into the module under test), not at the point of definition.
• patch.object: Use when you need to patch a method on an already-instantiated object or a class attribute without replacing the entire class. Prefer this over patch when the dependency is accessed via self.something.
• patch.dict: Use for patching os.environ, sys.modules, or any dictionary-like configuration store. Always clear=False (the default) to avoid wiping unrelated keys that other code depends on.
• Manual fakes: When a third-party interface is complex (e.g., S3 client with 40 methods), write a minimal in-memory fake class that implements only the methods your code uses. This is more maintainable than a MagicMock with 40 return_value assignments and is easier to reuse across tests.
• Spy pattern with wraps: Use MagicMock(wraps=real_function) to call through to the real implementation while still tracking calls. Useful for verifying that a real function is called with specific arguments without replacing its behavior.
• Dependency injection over patching: If you find yourself patching the same thing in 10 tests, refactor the source code to accept the dependency as a constructor argument. The test suite cost of excessive patching reveals a design problem.

4. Write Parametrized Tests with Intention

@pytest.mark.parametrize is the most underused and most misused pytest feature. Apply it with discipline:

• Use pytest.param(..., id="name") for every parameter: Test IDs like test_validate_email[email0] are useless in CI logs. Always name your cases: pytest.param("", id="empty-string"). This makes failure messages immediately diagnostic.
• Separate happy-path, edge-case, and error-case parameter lists: Don't mix valid and invalid inputs into one parametrize block. The behavior being tested is different, and the assertion is different. Use three parametrize blocks with three test functions.
• Use indirect parametrize for fixture-based variants: @pytest.mark.parametrize("user_fixture", ["admin_user", "guest_user"], indirect=True) passes the parameter to a fixture named user_fixture rather than directly to the test. This allows fixture setup logic to depend on the parameter.
• Stack decorators for a cartesian product: Two @pytest.mark.parametrize decorators create a cartesian product of cases. Use this only when you genuinely need all combinations. If you have 3 email types and 4 locale settings and need all 12 combinations tested, stacking is correct. If you only need 4 specific combinations, write them explicitly to avoid testing meaningless combinations.
• Use pytest.mark.skip and pytest.mark.xfail within parametrize: pytest.param("edge-input", id="known-bug", marks=pytest.mark.xfail(reason="GH-1234")) marks a single parametrize case as expected-to-fail without skipping the rest of the cases.
• Limit parametrize breadth to 10-15 cases per block: Beyond 15 cases, consider whether you are testing the same function boundary or whether you should add a fuzz testing tool like hypothesis for property-based testing of the same domain.

5. Configure and Write Async Tests Correctly

Async testing has specific pitfalls that cause tests to silently pass when they should fail, or to deadlock in CI.

• Install and configure pytest-asyncio: Add asyncio_mode = "auto" under [tool.pytest.ini_options] in pyproject.toml. This mode detects async def test_* functions automatically and runs them under an event loop. Without this, calling async def test_something() without @pytest.mark.asyncio silently skips the coroutine body.
• Event loop scope for fixtures: As of pytest-asyncio 0.21+, the event loop is scoped at the function level by default. If a session-scoped async fixture (e.g., a database connection pool) needs to share an event loop with tests, set asyncio_mode = "auto" and use @pytest_asyncio.fixture(scope="session") -- not @pytest.fixture. Mixing scopes incorrectly causes Event loop is closed errors in the second test.
• Mock async context managers: Code like async with session.begin(): ... requires a mock that supports __aenter__ and __aexit__. Use AsyncMock -- it automatically supports async context manager protocol. For AsyncMock instances, mock.__aenter__.return_value sets the value yielded by the async with block.
• Test timeout behavior: Use asyncio.wait_for(coroutine, timeout=5.0) inside the test to assert that an operation completes within a time budget. Never use time.sleep() in async tests -- it blocks the event loop and makes tests fragile. For code that uses asyncio.sleep() internally, patch it: patch("asyncio.sleep", new_callable=AsyncMock).
• Testing async generators: Use async for item in async_generator_function(): directly in the test body and collect results into a list for assertion. Do not try to use list() on an async generator -- it does not work synchronously.
• Mixing sync and async in one suite: pytest runs sync and async tests in the same suite without issue under asyncio_mode = "auto". The event loop is set up only for async tests. Do not convert sync tests to async unless the code under test is actually async.

6. Organize Test Files and Apply Markers

Test organization determines how useful the suite is at scale. A 500-test suite with no organization is nearly as bad as no tests.

• Mirror the source layout exactly: If your package is src/myapp/services/user_service.py, the test file is tests/unit/services/test_user_service.py. This makes it trivial to find tests for any module and is required by some coverage tools for accurate missing-line reporting.
• Apply custom markers strategically: Define markers in pyproject.toml under [tool.pytest.ini_options] markers = [...]. Standard markers to define: integration (requires external services), slow (runs longer than 1 second), smoke (critical path, run first), contract (pact or schema validation tests). Run subsets with pytest -m "not integration" in pre-commit and pytest -m "integration" in CI.
• Always run with --strict-markers: This flag causes pytest to fail with an error if a test uses a marker that is not registered. Add it permanently: addopts = "--strict-markers" in pyproject.toml.
• Use classes for grouping, not for sharing state: A class TestUserCreation: groups related tests under a descriptive namespace. Never use class-level attributes as shared mutable state between tests. If tests in a class share a fixture, pass it through method parameters -- do not assign it to self in a setup_method.
• Use conftest.py scoping to limit fixture visibility: A fixture defined in tests/unit/conftest.py is invisible to tests/integration/. This prevents integration fixtures (like database connections) from accidentally being used in unit tests, which would make them slow and require external services.
• Name test functions as declarative sentences: test_create_user_raises_duplicate_email_error_when_email_already_registered is better than test_duplicate_email. The verbose name is the documentation. When this test fails in CI at 2am, the name tells you exactly what broke without opening the file.

7. Configure Coverage and Quality Gates

Coverage without a quality gate is a vanity metric. Coverage with the wrong threshold is a false sense of security.

• Target 80-90% line coverage for business logic modules: 100% line coverage is often counterproductive -- it forces testing of trivial getters and if TYPE_CHECKING: blocks. Exclude those patterns via [tool.coverage.report] omit and exclude_lines in pyproject.toml.
• Use branch coverage, not just line coverage: coverage run --branch detects untested if/else branches that line coverage misses. Add branch = true under [tool.coverage.run].
• Configure fail_under: Set fail_under = 85 under [tool.coverage.report]. This makes pytest --cov exit with code 2 if coverage drops below 85%, blocking CI merges.
• Always exclude from coverage: if __name__ == "__main__": blocks, @overload decorated functions, abstract methods, and TYPE_CHECKING imports. Add these to exclude_lines as regex patterns.
• Use pytest-cov integration, not separate coverage run: pytest --cov=myapp --cov-report=term-missing --cov-report=xml runs coverage alongside tests and generates both terminal and XML reports in one step. The XML report feeds into SonarQube, Codecov, or similar tools.
• Review the coverage report for missing branches, not just lines: A line covered by a parametrize case that only tests the happy path will show as covered even though the error branch is never executed. Always read the branch miss report.

8. Enforce Test Quality with Static Analysis and Linting

Tests are production code. They deserve the same quality bar.

• Run mypy over test files: If source code is fully typed, test files should be too. MagicMock return values default to Any, which can hide type errors. Use spec= parameters to get typed mocks, and cast where necessary.
• Use ruff with pytest-specific rules enabled: Enable the PT rule set in ruff (the flake8-pytest-style rules). These catch common mistakes: using assert in fixtures, using pytest.raises outside a context manager, missing pytest.mark registration, and others.
• Lint for assert in fixture bodies: Fixtures that assert instead of raise hide setup failures as test failures. The PT ruff rules catch this.
• Use pytest --tb=short as the default traceback style: Add addopts = "--tb=short" to pyproject.toml. Full tracebacks in CI logs make failures harder to scan. Short tracebacks show the failing assertion line and the immediate call context -- enough to diagnose most failures.

---

Output Format

When helping a user design or review a test suite, produce output in this structure:

Test File Structure

tests/
├── conftest.py                        # project-wide fixtures
├── unit/
│   ├── conftest.py                    # unit-test-specific fixtures
│   ├── services/
│   │   └── test_{module_name}.py
│   └── models/
│       └── test_{model_name}.py
├── integration/
│   ├── conftest.py                    # DB connections, test clients
│   └── test_{integration_scenario}.py
└── contract/
    └── test_{service_name}_contract.py

pyproject.toml Configuration Block

[tool.pytest.ini_options]
asyncio_mode = "auto"
addopts = "--strict-markers --tb=short"
testpaths = ["tests"]
markers = [
    "integration: marks tests that require external services (deselect with '-m not integration')",
    "slow: marks tests that take longer than 1 second",
    "smoke: marks critical-path tests that should run first",
    "contract: marks schema or pact contract validation tests",
]

[tool.coverage.run]
source = ["src"]
branch = true
omit = ["*/__init__.py", "*/conftest.py"]

[tool.coverage.report]
fail_under = 85
exclude_lines = [
    "pragma: no cover",
    "if TYPE_CHECKING:",
    "if __name__ == .__main__.:",
    "@overload",
    "raise NotImplementedError",
]

Conftest.py Template

# tests/conftest.py
"""Project-wide test fixtures."""

import pytest
from unittest.mock import AsyncMock, MagicMock


# -- Dependency mocks (function-scoped -- fresh per test)

@pytest.fixture
def {dependency_name}_mock() -> MagicMock:
    """Isolated mock for {DependencyClass} with spec enforcement."""
    mock = MagicMock(spec={DependencyClass})
    mock.{primary_method}.return_value = {sensible_default}
    return mock


@pytest.fixture
def {async_dependency}_mock() -> AsyncMock:
    """Isolated async mock for {AsyncDependencyClass}."""
    mock = AsyncMock(spec={AsyncDependencyClass})
    mock.{async_method}.return_value = {sensible_default}
    return mock


# -- Subject under test

@pytest.fixture
def {subject}(
    {dependency_name}_mock: MagicMock,
    {async_dependency}_mock: AsyncMock,
) -> {SubjectClass}:
    """Fully wired {SubjectClass} with all dependencies mocked."""
    return {SubjectClass}(
        {dependency_arg}={dependency_name}_mock,
        {async_dependency_arg}={async_dependency}_mock,
    )

Test File Template

# tests/unit/services/test_{module}.py
"""Tests for {SubjectClass}: {one-sentence description of what is verified}."""

import pytest
from unittest.mock import MagicMock, call

from myapp.services.{module} import {SubjectClass}, {RelevantError}


class Test{PrimaryBehavior}:
    """Tests for the {specific behavior group} workflow."""

    def test_{behavior}_succeeds_when_{precondition}(
        self,
        {subject}: {SubjectClass},
        {dependency}_mock: MagicMock,
    ) -> None:
        """Verify {what the test proves in plain language}."""
        # Arrange
        {dependency}_mock.{method}.return_value = {specific_value}

        # Act
        result = {subject}.{method_under_test}({input})

        # Assert
        assert result.{field} == {expected_value}
        {dependency}_mock.{method}.assert_called_once_with({expected_args})

    def test_{behavior}_raises_{error}_when_{violation}(
        self,
        {subject}: {SubjectClass},
        {dependency}_mock: MagicMock,
    ) -> None:
        """Verify {ErrorClass} is raised when {condition}."""
        {dependency}_mock.{method}.return_value = {value_triggering_error}

        with pytest.raises({RelevantError}, match="{expected_message_substring}"):
            {subject}.{method_under_test}({triggering_input})

    @pytest.mark.parametrize(
        "{input_param},{expected_param}",
        [
            pytest.param({input_1}, {expected_1}, id="{case_name_1}"),
            pytest.param({input_2}, {expected_2}, =),
            pytest.param(),
            pytest.param(),
        ],
    )
     {behavior}_handles_various_{input_type}(
        ,
        {subject}: {SubjectClass},
        {input_param}: {InputType},
        {expected_param}: {ExpectedType},
    ) -> :
        
        result = {subject}.{method_under_test}({input_param})
         result == {expected_param}


 {SecondaryBehavior}:
    

      {async_behavior}_awaits_{dependency}(
        ,
        {subject}: {SubjectClass},
        {async_dependency}_mock: AsyncMock,
    ) -> :
        
         {subject}.{async_method}({})

        {async_dependency}_mock.{async_method}.assert_awaited_once_with(
            {expected_kwarg}={expected_value}
        )

---

Rules

1. Always use spec= when creating MagicMock instances. MagicMock(spec=UserRepository) raises AttributeError if test code accesses an attribute that does not exist on the real class. Without spec=, typos in mock attribute access silently return new MagicMock objects, letting tests pass while the production code has a bug.

2. Never patch at the definition site -- always patch at the import site. If user_service.py contains from myapp.email import EmailSender, patch myapp.user_service.EmailSender, not myapp.email.EmailSender. Patching the wrong location leaves the production reference unchanged and makes the patch invisible to the code under test.

3. Never use unittest.TestCase in new pytest tests. TestCase disables pytest features including fixtures, pytest.raises, and parametrize. It forces setUp/tearDown over composable fixtures. Always write plain functions or plain classes (no inheritance) for new test code. Existing TestCase classes can coexist -- do not rewrite them, just stop adding to that pattern.

4. Always use pytest.raises as a context manager with a match= argument. with pytest.raises(ValueError, match="invalid email"): verifies both the exception type and that the message contains the expected substring (matched as a regex). Without match=, a ValueError raised for a completely different reason will make the test pass.

5. Never allow test functions to exceed 30 lines. A test longer than 30 lines is either testing multiple behaviors (split it) or has setup that belongs in a fixture (extract it). Long tests are a reliable smell for poor fixture design and poor separation of concerns.

6. Always use assert_called_once_with over assert_called_with. assert_called_with only checks the most recent call, not the number of calls. A function called twice with different arguments passes assert_called_with on the second call. Use assert_called_once_with to verify both that the call happened and that it happened exactly once.

---

Edge Cases

Legacy unittest.TestCase Coexistence

When a codebase has hundreds of existing TestCase classes, do not rewrite them. pytest discovers and runs TestCase subclasses natively. Write all new tests as plain pytest functions in separate files. Gradually migrate TestCase methods only when you are already modifying that test class for another reason. To use pytest fixtures inside TestCase methods, use pytest.mark and self. attribute assignment in setUp -- but be aware this is a transitional pattern, not a final destination. The goal is to stop the proliferation of TestCase usage and let the pattern die naturally.

Async Event Loop Conflicts with Session-Scoped Fixtures

When a session-scoped async fixture (e.g., a database connection pool) is combined with function-scoped async tests, pytest-asyncio must share the event loop across the session. As of pytest-asyncio 0.21, the correct approach is to define a session-scoped event loop fixture explicitly:

import asyncio
import pytest

@pytest.fixture(scope="session")
def event_loop():
    loop = asyncio.new_event_loop()
    yield loop
    loop.close()

Without this, a session-scoped async fixture creates its event loop, completes setup, and the loop is closed before any test runs -- causing RuntimeError: Event loop is closed on the first test. This fixture override tells pytest-asyncio to use one loop for the session. Note: this approach is deprecated in pytest-asyncio 0.23+ in favor of loop_scope fixture parameters -- check the installed version before applying.

Patching Properties and Cached Properties

@property and @functools.cached_property cannot be patched using patch directly because MagicMock does not implement the descriptor protocol. Use patch.object with new_callable=PropertyMock:

from unittest.mock import patch, PropertyMock

with patch.object(MyClass, "my_property", new_callable=PropertyMock) as mock_prop:
    mock_prop.return_value = 42
    result = obj.my_property
    assert result == 42

For cached_property, additionally clear the instance cache after patching: del obj.__dict__["my_property"] resets the cached value so the mock takes effect on the next access.

Testing Code That Uses os.environ or Config at Import Time

Code that reads os.environ at import time (at module scope) cannot be patched after import -- the value is already captured. This is a design problem in production code, but the test-side workaround is to force module reimport under the patched environment:

import importlib
from unittest.mock import patch

def test_config_with_custom_env():
    with patch.dict("os.environ", {"DATABASE_URL": "sqlite:///:memory:"}):
        import myapp.config
        importlib.reload(myapp.config)
        assert myapp.config.DATABASE_URL == "sqlite:///:memory:"

The correct long-term fix is to move environment reads into a function or class that is called at runtime, not at import time. Document this in the test with a comment pointing to the design smell.

Flaky Tests from Non-Deterministic Ordering

pytest does not guarantee test execution order within a file unless you use pytest-ordering. If tests appear to be order-dependent, it indicates shared mutable state. Common causes:

• A class-level list or dictionary being mutated by one test and read by another
• A module-level singleton being modified
• A database not being rolled back between tests

Diagnose with pytest --randomly-seed=12345 (requires pytest-randomly) to run in a consistent but non-alphabetical order. Use pytest -p no:randomly to run in collection order for comparison. Never add retry logic (via pytest-retry or pytest-rerunfailures) to hide a flaky test -- fix the isolation. The only legitimate use of pytest-rerunfailures is for tests that exercise genuinely non-deterministic external services (network calls in integration tests), and those should be marked @pytest.mark.flaky(reruns=3) with a comment explaining why.

Testing Pydantic Models and Validators

Pydantic v2 models validate on construction, not on assignment by default. When testing Pydantic models, test the model's behavior -- validation, computed fields, and serialization -- not its internal structure:

import pytest
from pydantic import ValidationError
from myapp.schemas import CreateUserRequest

def test_rejects_invalid_email():
    with pytest.raises(ValidationError) as exc_info:
        CreateUserRequest(email="not-an-email", name="Alice")
    errors = exc_info.value.errors()
    assert len(errors) == 1
    assert errors[0]["loc"] == ("email",)
    assert errors[0]["type"] == "value_error"

def test_normalizes_email_to_lowercase():
    request = CreateUserRequest(email="[e-mail]", name="Alice")
    assert request.email == "[e-mail]"

Never access model.__fields__ or model.model_fields in tests -- those are implementation details. Test what the model does, not how it is defined.

Parallel Test Execution with pytest-xdist

Running tests with pytest -n auto (requires pytest-xdist) distributes tests across CPU cores. This breaks any test that uses a shared resource without locking:

• Database: Each worker needs its own database schema or isolated schema prefix. Use worker_id from the xdist fixture to generate unique schema names: f"test_{worker_id}".
• Port binding: Integration tests that start a server on a fixed port will conflict. Use port=0 (OS-assigned) and read the assigned port after binding.
• File system: Tests writing to overlapping file paths will race. Use tmp_path -- each test gets a unique path even under xdist.
• Session-scoped fixtures: scope="session" fixtures run once per worker, not once per session when using xdist. Use scope="session" with pytest-xdist's xdist_group marker to pin related tests to the same worker if they must share session state.

---

Example

Input: "I have a PaymentProcessor class that depends on a PaymentGateway (async external API client) and a TransactionRepository (database layer). It has a process_payment method that validates the amount, calls the gateway, and saves the transaction. How do I write comprehensive tests for this?"

Assumed source code structure:

# src/myapp/services/payment_processor.py
from decimal import Decimal
from myapp.gateways.payment_gateway import PaymentGateway, GatewayError
from myapp.repositories.transaction_repo import TransactionRepository
from myapp.models import Transaction, TransactionStatus

class PaymentAmountError(ValueError):
    pass

class PaymentProcessor:
    def __init__(
        self,
        gateway: PaymentGateway,
        repo: TransactionRepository,
    ) -> None:
        self._gateway = gateway
        self._repo = repo

    async def process_payment(
        self, user_id: str, amount: Decimal, currency: str
    ) -> Transaction:
        if amount <= Decimal("0"):
            raise PaymentAmountError(f"Amount must be positive, got {amount}")
        if amount > Decimal("10000"):
            raise PaymentAmountError(f"Amount exceeds maximum of 10000, got {amount}")

        gateway_ref = await self._gateway.charge(
            user_id=user_id, amount=amount, currency=currency
        )
        transaction = await self._repo.create(
            user_id=user_id,
            amount=amount,
            currency=currency,
            gateway_ref=gateway_ref,
            status=TransactionStatus.COMPLETED,
        )
         transaction

Output:

# pyproject.toml (relevant sections)
[tool.pytest.ini_options]
asyncio_mode = "auto"
addopts = "--strict-markers --tb=short --cov=src --cov-report=term-missing"
testpaths = ["tests"]
markers = [
    "integration: requires external services or database",
    "slow: runs longer than 1 second",
]

[tool.coverage.run]
branch = true
source = ["src"]

[tool.coverage.report]
fail_under = 85
exclude_lines = [
    "pragma: no cover",
    "if TYPE_CHECKING:",
    "raise NotImplementedError",
]

# tests/unit/services/conftest.py
"""Fixtures for PaymentProcessor unit tests."""

from decimal import Decimal
from unittest.mock import AsyncMock, MagicMock

import pytest

from myapp.gateways.payment_gateway import PaymentGateway
from myapp.models import Transaction, TransactionStatus
from myapp.repositories.transaction_repo import TransactionRepository
from myapp.services.payment_processor import PaymentProcessor


@pytest.fixture
def gateway_mock() -> AsyncMock:
    """Async mock for PaymentGateway with spec enforcement.

    Default: charge succeeds and returns a gateway reference string.
    Override return_value or side_effect per-test for error scenarios.
    """
    mock = AsyncMock(spec=PaymentGateway)
    mock.charge.return_value = "gw_ref_abc123"
    return mock


@pytest.fixture
def repo_mock() -> AsyncMock:
    """Async mock for TransactionRepository with spec enforcement.

    Default: create returns a completed Transaction with predictable IDs.
    """
    mock = AsyncMock(spec=TransactionRepository)
    mock.create.return_value = Transaction(
        id="txn_001",
        user_id="usr_42",
        amount=Decimal("99.99"),
        currency="USD",
        gateway_ref="gw_ref_abc123",
        status=TransactionStatus.COMPLETED,
    )
    return mock


@pytest.fixture
def processor(gateway_mock: AsyncMock, repo_mock: AsyncMock) -> PaymentProcessor:
    """PaymentProcessor wired with all dependencies mocked."""
     PaymentProcessor(gateway=gateway_mock, repo=repo_mock)

# tests/unit/services/test_payment_processor.py
"""Tests for PaymentProcessor.process_payment behavior.

Coverage targets:
- Happy path: valid amount triggers gateway charge and repo create
- Amount validation: zero, negative, above maximum, boundary values
- Gateway errors: GatewayError propagation behavior
- Call ordering: gateway charged before repo saved (no orphaned saves)
"""

from decimal import Decimal
from unittest.mock import AsyncMock, call

import pytest

from myapp.gateways.payment_gateway import GatewayError
from myapp.models import TransactionStatus
from myapp.services.payment_processor import PaymentAmountError, PaymentProcessor


class TestProcessPaymentHappyPath:
    """Tests for the successful payment processing workflow."""

    async def test_returns_completed_transaction_for_valid_payment(
        self,
        processor: PaymentProcessor,
        repo_mock: AsyncMock,
    ) -> None:
        """Verify process_payment returns the repository-created transaction."""
        result = await processor.process_payment(
            user_id="usr_42",
            amount=Decimal("99.99"),
            currency="USD",
        )

        assert result.id == "txn_001"
        assert result.status == TransactionStatus.COMPLETED
        assert result.gateway_ref == "gw_ref_abc123"

    async def test_charges_gateway_with_correct_arguments(
        self,
        processor: PaymentProcessor,
        gateway_mock: AsyncMock,
    ) -> :
        
         processor.process_payment(
            user_id=,
            amount=Decimal(),
            currency=,
        )

        gateway_mock.charge.assert_awaited_once_with(
            user_id=,
            amount=Decimal(),
            currency=,
        )

      () -> :
        
        gateway_mock.charge.return_value = 

         processor.process_payment(
            user_id=,
            amount=Decimal(),
            currency=,
        )

        repo_mock.create.assert_awaited_once_with(
            user_id=,
            amount=Decimal(),
            currency=,
            gateway_ref=,   
            status=TransactionStatus.COMPLETED,
        )

      () -> :
        
        call_order = []
        gateway_mock.charge.side_effect =  **_: call_order.append()  
        repo_mock.create.side_effect =  **_: call_order.append()

        
        
         myapp.models  Transaction
        repo_mock.create.side_effect =   

          ():
            call_order.append()
             

          ():
            call_order.append()
             repo_mock.create.return_value

        gateway_mock.charge.side_effect = _charge
        repo_mock.create.side_effect = _create

         processor.process_payment(
            user_id=, amount=Decimal(), currency=
        )

         call_order == [, ], (
            
        )


 :
    


      () -> :
        
         pytest.raises(PaymentAmountError, =expected_error_fragment):
             processor.process_payment(
                user_id=, amount=amount, currency=
            )


      () -> :
        
        
        result =  processor.process_payment(
            user_id=, amount=amount, currency=
        )
         result   

      () -> :
        
         pytest.raises(PaymentAmountError):
             processor.process_payment(
                user_id=, amount=Decimal(), currency=
            )

        gateway_mock.charge.assert_not_awaited()


 :
    

      () -> :
        
        gateway_mock.charge.side_effect = GatewayError()

         pytest.raises(GatewayError, =):
             processor.process_payment(
                user_id=, amount=Decimal(), currency=
            )

        repo_mock.create.assert_not_awaited()

      () -> :
        
         asyncio
        gateway_mock.charge.side_effect = GatewayError()

         pytest.raises(GatewayError, =):
             processor.process_payment(
                user_id=, amount=Decimal(), currency=
            )

# tests/integration/test_payment_processor_integration.py
"""Integration tests for PaymentProcessor with real repository.

These tests require a running PostgreSQL instance and are excluded from
pre-commit hooks via: pytest -m "not integration"
"""

import pytest
from decimal import Decimal

pytestmark = pytest.mark.integration


@pytest.fixture(scope="module")
async def db_engine():
    """Create a real database engine for the integration test module."""
    from sqlalchemy.ext.asyncio import create_async_engine
    engine = create_async_engine("postgresql+asyncpg://test:test@localhost/test_db")
    yield engine
    await engine.dispose()


@pytest.fixture
async def db_session(db_engine):
    """Provide a rolled-back transaction per test for isolation."""
    from sqlalchemy.ext.asyncio import AsyncSession
    async with AsyncSession(db_engine) as session:
        async with session.begin():
            yield session
            await session.rollback()


@pytest.mark.integration
async def test_payment_creates_persisted_transaction(db_session, gateway_mock):
    """Verify the full creation workflow persists a transaction retrievable from DB."""
    from myapp.repositories.transaction_repo import SqlAlchemyTransactionRepository
     myapp.services.payment_processor  PaymentProcessor

    repo = SqlAlchemyTransactionRepository(session=db_session)
    processor = PaymentProcessor(gateway=gateway_mock, repo=repo)

    result =  processor.process_payment(
        user_id=,
        amount=Decimal(),
        currency=,
    )

    
     myapp.models  Transaction
    saved =  db_session.get(Transaction, result.)
     saved   
     saved.amount == Decimal()
     saved.currency == 

This test suite demonstrates the complete pattern:

• conftest.py provides spec-constrained AsyncMock fixtures, eliminating repeated mock setup across 10 test functions
• Happy-path tests each verify exactly one observable behavior (return value, call arguments, call ordering) -- not all three in one test
• Amount validation uses two separate parametrize blocks -- one for rejections, one for acceptances -- with explicit id= names on every case
• Gateway error tests verify the absence of calls (assert_not_awaited) as much as the presence, preventing silent data corruption from partially completed workflows
• Integration tests are in a separate file, marked with pytestmark = pytest.mark.integration, use a transaction-rollback pattern for isolation, and verify behavior through the database rather than the return value alone
• The pyproject.toml configuration enforces branch coverage at 85%, strict markers, and auto