Testing Strategy

Testing Strategy

What I Do

Provide universal testing strategies and best practices that apply across different programming languages and project types.

Universal Testing Framework

Test Organization

# Universal test directory structure
tests/
├── unit/              # Unit tests
│   ├── core/          # Core functionality tests
│   └── utils/         # Utility function tests
├── integration/       # Integration tests
├── e2e/               # End-to-end tests
├── performance/       # Performance tests
├── conftest.py        # Shared fixtures
└── test_data/         # Test data files

Test Coverage Standards

Minimum Coverage Targets:

• Unit Tests: 90%+ code coverage
• Integration Tests: 80%+ scenario coverage
• End-to-End Tests: Critical user journey coverage
• Performance Tests: Baseline performance metrics

# Universal test coverage monitoring
import pytest
import coverage

def run_tests_with_coverage():
    """Run tests with coverage measurement"""
    cov = coverage.Coverage()
    cov.start()

    # Run pytest
    exit_code = pytest.main([
        "--cov=src",
        "--cov-report=term-missing",
        "--cov-fail-under=90",
        "tests/"
    ])

    cov.stop()
    cov.save()
    return exit_code

When to Use Me

Use this skill when:

• Setting up testing for new projects
• Standardizing testing across teams
• Creating reusable testing patterns
• Implementing quality assurance processes

Universal Testing Examples

Test Fixtures

# Universal test fixture patterns
import pytest
from typing import Generator

@pytest.fixture(scope="module")
def database_connection() -> Generator:
    """Universal database fixture"""
    # Setup
    conn = create_test_database()
    yield conn
    # Teardown
    conn.close()
    cleanup_test_database()

@pytest.fixture
def sample_data():
    """Universal sample data fixture"""
    return {
        "valid_input": get_valid_input(),
        "edge_cases": get_edge_cases(),
        "invalid_input": get_invalid_input()
    }

Parameterized Testing

# Universal parameterized testing
import pytest

@pytest.mark.parametrize("input,expected", [
    ([1, 2, 3], 6),      # Normal case
    ([], 0),             # Empty input
    ([-1, 0, 1], 0),     # Mixed values
    ([1.5, 2.5], 4.0)    # Float values
])
def test_sum_function(input, expected):
    """Test sum function with various inputs"""
    assert sum(input) == expected

Mocking and Isolation

# Universal mocking patterns
from unittest.mock import patch, MagicMock
import requests

def test_api_call():
    """Test API calls with mocking"""
    # Mock external API
    mock_response = MagicMock()
    mock_response.json.return_value = {"status": "success"}

    with patch("requests.get", return_value=mock_response):
        result = make_api_call()
        assert result == {"status": "success"}
        requests.get.assert_called_once_with("https://api.example.com/data")

Performance Testing

# Universal performance testing
import time
import pytest

@pytest.mark.performance
def test_processing_speed():
    """Test processing speed meets requirements"""
    start_time = time.time()

    # Run the operation
    result = process_large_dataset()

    duration = time.time() - start_time
    assert duration < 5.0, f"Processing took {duration:.2f}s, expected <5.0s"
    assert result.is_valid()

Best Practices

1. Consistency: Apply same testing patterns across projects
2. Automation: Integrate testing into CI/CD pipelines
3. Isolation: Test components in isolation
4. Documentation: Document testing approaches clearly

Compatibility

Applies to:

• All programming languages
• Any software project type
• Cross-project testing standardization
• Organizational quality assurance