masto-rss/TEST_SUMMARY.md

Test Implementation Summary

Overview

Comprehensive test suite designed for the Masto-RSS bot with unit tests, integration tests, and automated CI/CD testing via GitHub Actions.

Files Created

1. Core Refactoring

• bot.py - Refactored core functionality into testable MastodonRSSBot class

  • Separated concerns (parsing, posting, state management)
  • Dependency injection for easier testing
  • Error handling and logging
  • Type hints for better code clarity

• main.py - Simplified entry point

  • Loads configuration from environment
  • Instantiates and runs the bot
  • Clean separation from core logic

2. Test Files

test_bot.py - Unit Tests

Contains 20+ unit tests covering:

• Bot initialization with configuration
• Loading/saving processed entries (with and without existing files)
• Directory creation for state files
• Status formatting from feed entries
• Mastodon posting (success and failure cases)
• Feed parsing (success, errors, malformed data)
• Processing new entries (all new, partially processed, no entries)
• Handling entries without URLs
• Failed posts don't get marked as processed
• Main entry point configuration loading

Coverage: Core business logic, edge cases, error handling

test_integration.py - Integration Tests

Contains 10+ integration tests covering:

• End-to-end RSS to Mastodon flow
• Real RSS 2.0 feed parsing
• Real Atom feed parsing
• State persistence across bot runs
• Incremental feed updates (new entries added over time)
• Network error handling (500 errors, timeouts)
• Malformed XML handling
• Different visibility levels (public, unlisted, private, direct)
• Rate limiting error handling
• Retry mechanisms

Coverage: Component integration, external service mocking, data flow

3. Configuration Files

pytest.ini

• Test discovery patterns
• Coverage settings (80% minimum)
• Output formatting
• Test markers (unit, integration, slow)
• Coverage exclusions

pyproject.toml

Test dependencies (managed by uv):

• pytest - Testing framework
• pytest-cov - Coverage plugin
• pytest-mock - Mocking utilities
• responses - HTTP mocking for integration tests
• flake8 - Linting
• black - Code formatting
• mypy - Type checking
• coverage - Coverage reporting

.gitignore

Updated to exclude:

• Test artifacts (.pytest_cache/, htmlcov/, coverage.xml)
• Python cache files
• Virtual environments
• IDE configurations

4. CI/CD Pipeline

.github/workflows/test.yml

Comprehensive GitHub Actions workflow with 5 jobs:

1. Unit Tests

   • Runs on Python 3.10, 3.11, 3.12 (matrix)
   • Executes unit tests with coverage
   • Uploads coverage to Codecov

2. Integration Tests

   • Runs on Python 3.10, 3.11, 3.12 (matrix)
   • Executes integration tests
   • Uploads coverage to Codecov

3. Code Quality

   • Ruff linting and formatting verification
   • Mypy type checking

4. Docker Build Test

   • Builds Docker image
   • Verifies Python installation
   • Checks dependencies are installed

5. All Tests Pass

   • Requires all previous jobs to succeed
   • Provides single status check for PRs

5. Documentation

TESTING.md

Comprehensive testing guide covering:

• Test architecture explanation
• Running tests locally (all, specific, by marker)
• Coverage report generation
• GitHub Actions CI/CD workflow details
• Test coverage requirements (80% minimum)
• Code quality standards
• Writing new tests (templates provided)
• Troubleshooting common issues
• Best practices

README.md

Updated with:

• Test status badge
• Link to testing documentation

Test Statistics

Coverage Targets

• Minimum: 80% code coverage
• Measured: bot.py and main.py
• Excluded: Test files, virtual environments

Test Count

• Unit Tests: 20+ tests
• Integration Tests: 10+ tests
• Total: 30+ tests

Python Versions Tested

• Python 3.10
• Python 3.11
• Python 3.12

Key Testing Features

Mocking Strategy

• Mastodon API: Mocked using unittest.mock to avoid real API calls
• RSS Feeds: Mocked using responses library with realistic XML
• File System: Uses temporary files that are cleaned up automatically

Test Data

• Realistic RSS 2.0 and Atom feed examples
• Multiple entry scenarios (new, processed, malformed)
• Error conditions (network failures, API errors, rate limits)

Continuous Integration

• Runs on every push to main
• Runs on all pull requests
• Parallel test execution across Python versions
• Automatic coverage reporting
• Docker image validation

Running Tests

Locally

# Install dependencies
pip install -r requirements-test.txt

# Run all tests
pytest

# Run with coverage
pytest --cov=bot --cov=main --cov-report=html

# Run specific tests
pytest test_bot.py  # Unit tests only
pytest test_integration.py  # Integration tests only

In CI/CD

Tests run automatically via GitHub Actions:

• Trigger: Push to main or pull request
• Duration: ~5-10 minutes
• Matrix: 3 Python versions × 2 test types = 6 parallel jobs
• Plus: Code quality and Docker build validation

Benefits

1. Code Quality: Ensures all changes are tested before merging
2. Regression Prevention: Catches bugs before they reach production
3. Documentation: Tests serve as executable documentation
4. Confidence: Safe to refactor with comprehensive test coverage
5. Type Safety: Mypy catches type-related bugs early
6. Code Style: Black and flake8 enforce consistency

Future Enhancements

Potential additions:

• Performance/load testing
• End-to-end tests with real Mastodon test instance
• Security scanning (Bandit, Safety)
• Mutation testing (mutmut)
• Property-based testing (Hypothesis)
• Contract testing for API interactions

Maintenance

• Add tests for all new features
• Update tests when changing behavior
• Keep coverage above 80%
• Run tests before committing
• Review test failures in CI before merging