python3-test-design

Python Test Design Skill

Guidance for designing pytest test suites with modern Python 3.11+ patterns.

When to Use This Skill

Use this skill for test design decisions:

• Planning test suite architecture
• Choosing testing strategies (unit, integration, e2e)
• Designing fixture hierarchies
• Determining coverage strategies
• Planning property-based testing approach

For test implementation, use the python-pytest-architect agent instead.

Test Design Principles

Consult ../python3-development/references/python3-standards.md when aligning test strategy with shared plugin testing norms. The strategies below supplement that document; they do not replace it.

Test Pyramid

Structure test suites following the test pyramid:

        /\
       /  \     E2E (few, slow, high confidence)
      /----\
     /      \   Integration (moderate, medium speed)
    /--------\
   /          \ Unit (many, fast, focused)
  /------------\

Distribution targets:

• Unit tests: 70-80% of test count
• Integration tests: 15-25% of test count
• E2E tests: 5-10% of test count

Test Naming Convention

Use behavioral naming that describes what is being tested:

# Pattern: test_{function}_{scenario}_{expected_result}

def test_validate_email_with_invalid_format_raises_validation_error():
    """Validate that malformed emails are rejected."""
    ...

def test_process_payment_when_insufficient_funds_returns_declined():
    """Payment processing declines when balance is insufficient."""
    ...

AAA Pattern (Arrange-Act-Assert)

Structure every test with clear sections:

def test_user_registration_creates_account():
    # Arrange
    user_data = {"email": "test@example.com", "name": "Test User"}
    repository = InMemoryUserRepository()
    service = UserService(repository)

    # Act
    result = service.register(user_data)

    # Assert
    assert result.success is True
    assert repository.count() == 1

Test Strategy Selection

When to Use Unit Tests

• Pure functions with no side effects
• Business logic validation
• Data transformation
• Algorithm correctness

When to Use Integration Tests

• Database interactions
• External API calls
• File system operations
• Multi-component workflows

When to Use Property-Based Tests

• Functions with mathematical properties (commutativity, associativity)
• Parsers and serializers (round-trip property)
• Data validation (valid inputs always accepted)
• State machines (invariants maintained)

from hypothesis import given, strategies as st

@given(st.lists(st.integers()))
def test_sort_maintains_length(data):
    """Sorting preserves all elements."""
    result = sorted(data)
    assert len(result) == len(data)

When to Use BDD Tests

• User-facing features with acceptance criteria
• Requirements traceability needed
• Non-technical stakeholder visibility
• Complex user workflows

Fixture Design

Fixture Hierarchy

Organize fixtures by scope and purpose:

conftest.py (root)
├── Session fixtures (db connections, servers)
├── Module fixtures (shared test data)
└── Function fixtures (isolated per-test data)

tests/
├── conftest.py              # Shared fixtures
├── unit/
│   └── conftest.py          # Unit-specific fixtures
└── integration/
    └── conftest.py          # Integration-specific fixtures

Factory Pattern for Test Data

Use factories for complex test objects:

import factory
from datetime import datetime, UTC

class UserFactory(factory.Factory):
    class Meta:
        model = User

    id = factory.Sequence(lambda n: n)
    email = factory.LazyAttribute(lambda o: f"user{o.id}@example.com")
    created_at = factory.LazyFunction(lambda: datetime.now(UTC))

Coverage Strategy

Minimum Coverage Requirements

Code Type	Minimum Coverage
Business logic	90%
Standard code	80%
Scripts/utilities	70%
Critical paths	95% + mutation testing

Coverage Configuration

# pyproject.toml
[tool.coverage.run]
branch = true
source = ["packages"]
omit = ["**/tests/**", "**/__pycache__/**"]

[tool.coverage.report]
fail_under = 80
exclude_lines = [
    "pragma: no cover",
    "if TYPE_CHECKING:",
    "raise NotImplementedError",
]

Mutation Testing for Critical Code

Apply mutation testing to security-critical and payment-related code:

# Run mutation tests on auth module
uv run mutmut run --paths-to-mutate=packages/auth/

# View surviving mutants
uv run mutmut results

Target: >90% mutation score for critical code paths

Test Directory Structure

tests/
├── conftest.py              # Shared fixtures, pytest plugins
├── unit/                    # Fast, isolated tests
│   ├── test_validators.py
│   └── test_models.py
├── integration/             # Tests with external dependencies
│   ├── test_database.py
│   └── test_api_client.py
├── e2e/                     # End-to-end workflows
│   └── test_user_flows.py
├── fixtures/                # Test data files
│   ├── sample_config.yaml
│   └── test_data.json
└── conftest.py              # Root-level configuration

Related Resources

• Agent: Use python3-development:python-pytest-architect for test implementation
• Skill: python3-development for general Python patterns
• Command: /python3-development:modernpython for modern Python syntax reference