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15. Testing Strategy

Status

Proposed

Context

CodeFlow requires a comprehensive testing strategy to ensure:

  • Code quality and reliability
  • Prevention of regressions
  • Documentation of expected behavior
  • Confidence in continuous deployment
  • Efficient developer workflow

Decision

We will implement a multi-layered testing strategy with the following components:

1. Test Pyramid Implementation

1.1 Unit Tests (60%)

  • Scope: Individual functions and classes
  • Framework: pytest
  • Coverage Target: 80%+ line coverage
  • Mocking: unittest.mock or pytest-mock for external dependencies
  • Speed: Sub-second test suite execution
# Example unit test with pytest
def test_parse_github_event():
    # Arrange
    event_data = {"action": "opened", "pull_request": {"number": 123}}

    # Act
    result = parse_github_event(event_data)

    # Assert
    assert result.pr_number == 123
    assert result.action == "opened"

1.2 Integration Tests (30%)

  • Scope: Component interactions
  • Framework: pytest with fixtures
  • Focus: API endpoints, database operations, external service integrations
  • Data Management: Factory Boy for test data
  • Isolation: Test containers for dependencies

1.3 End-to-End Tests (10%)

  • Scope: Complete user flows
  • Framework: Playwright for browser automation
  • Focus: Critical user journeys
  • Environment: Staging-like environment
  • Data: Seeded test data

2. Test Automation

2.1 CI/CD Pipeline

  • Unit Tests: Run on every push
  • Integration Tests: Run on pull requests
  • E2E Tests: Run on merge to main
  • Performance Tests: Scheduled daily

2.2 Test Data Management

  • Factories: Reusable test data factories
  • Fixtures: Database state setup/teardown
  • Snapshots: For complex output verification

3. Quality Gates

3.1 Code Coverage

  • Minimum 80% line coverage
  • PRs blocked if coverage decreases
  • Coverage reports in CI

3.2 Static Analysis

  • Type checking with mypy
  • Code style with Black and isort
  • Security scanning with Bandit
  • Dependency checking with safety

4. Performance Testing

  • Load Testing: Locust for API endpoints
  • Stress Testing: Identify breaking points
  • Baseline Metrics: Track performance over time

Consequences

  • Improved Quality: Fewer bugs in production
  • Faster Development: Quick feedback loops
  • Higher Confidence: Safe deployments
  • Technical Debt: Requires maintenance
  • Initial Investment: Setup time required

Implementation Plan

  1. Set up test infrastructure
  2. Implement test patterns and utilities
  3. Add tests for critical paths
  4. Enforce quality gates in CI/CD
  5. Monitor and improve test effectiveness

Monitoring and Metrics

  • Test execution time
  • Flaky test rate
  • Code coverage trends
  • Test failure analysis
  • Time to fix failing tests