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Testing Guide

This guide covers testing practices and procedures for FlavumHive development.

Testing Philosophy​

Our testing approach focuses on:

  • Comprehensive test coverage
  • Test-driven development
  • Integration testing
  • Automated testing
  • Performance testing

Test Structure​

Unit Tests​

def test_twitter_post():
    """Test Twitter posting functionality."""
    handler = TwitterHandler(config)
    result = handler.post_tweet("Test message")
    assert result.success
    assert result.tweet_id is not None

Integration Tests​

@pytest.mark.integration
def test_platform_interaction():
    """Test cross-platform interaction."""
    twitter = TwitterHandler(config)
    reddit = RedditHandler(config)
    
    # Post content
    tweet = twitter.post_tweet("Test")
    reddit_post = reddit.create_post(
        "test", "Title", "Content"
    )
    
    assert tweet.success
    assert reddit_post.success

Mock Tests​

def test_rate_limiting():
    """Test rate limiting with mocks."""
    with patch('flavumhive.RateLimiter') as mock:
        mock.check_limit.return_value = False
        handler = TwitterHandler(config)
        result = handler.post_tweet("Test")
        assert not result.success

Running Tests​

Basic Usage​

# Run all tests
pytest

# Run specific test file
pytest tests/test_twitter.py

# Run specific test
pytest tests/test_twitter.py::test_post_tweet

Test Options​

# Run with coverage
pytest --cov=flavumhive tests/

# Generate coverage report
pytest --cov=flavumhive --cov-report=html tests/

# Run marked tests
pytest -m "integration"

Writing Tests​

Test File Structure​

"""Tests for Twitter handler functionality."""
import pytest
from flavumhive import TwitterHandler

@pytest.fixture
def twitter_handler():
    """Create Twitter handler fixture."""
    config = load_test_config()
    return TwitterHandler(config)

def test_feature(twitter_handler):
    """Test specific feature."""
    result = twitter_handler.some_method()
    assert result.success

Test Categories​

  1. Unit Tests

    • Single component
    • Isolated functionality
    • Mock dependencies
    • Quick execution

  2. Integration Tests

    • Multiple components
    • Real dependencies
    • End-to-end flows
    • Slower execution

  3. Performance Tests

    • Load testing
    • Stress testing
    • Benchmarking
    • Resource usage

Best Practices​

Test Design​

  • Test one thing per test
  • Use descriptive names
  • Include edge cases
  • Test error conditions

Test Implementation​

  • Use fixtures
  • Mock external services
  • Clean up resources
  • Handle async properly

Test Maintenance​

  • Keep tests updated
  • Remove obsolete tests
  • Refactor when needed
  • Document changes

Common Patterns​

Setup and Teardown​

@pytest.fixture(autouse=True)
def setup_teardown():
    """Setup and teardown for tests."""
    # Setup
    init_test_db()
    yield
    # Teardown
    cleanup_test_db()

Parametrized Tests​

@pytest.mark.parametrize("input,expected", [
    ("test1", True),
    ("test2", False),
    ("test3", True)
])
def test_validation(input, expected):
    """Test input validation."""
    result = validate_input(input)
    assert result == expected

Debugging Tests​

Debug Tools​

# Run with debug info
pytest -vv

# Run with print output
pytest -s

# Debug on error
pytest --pdb

Common Issues​

  1. Resource Cleanup

    • Close connections
    • Remove test files
    • Reset state

  2. Async Testing

    • Use async fixtures
    • Handle coroutines
    • Manage event loop

  3. Mock Issues

    • Verify call counts
    • Check arguments
    • Reset mocks

Next Steps​