quality-engineering

Quality Engineering

Guidelines for QA processes, test automation, and performance engineering.

When to Use

• Designing test strategies for new features
• Setting up or improving test automation
• Performance testing and optimization
• Capacity planning and load testing
• Establishing quality metrics
• Fixing flaky tests
• Improving test coverage
• Debugging integration test failures

When NOT to Use

• Writing specific test cases (follow TDD patterns)
• Language-specific testing (use language skills)
• End-to-end test implementation details

Quality Philosophy

• Prevention over detection - Engage early to prevent defects
• Test behavior, not implementation - Focus on observable outcomes
• No failing builds - Never merge broken code
• Continuous improvement - Regularly refine processes

Test Strategy

Test Pyramid

       /\
      /E2E\      ← Few, critical paths
     /------\
    /  Integ  \   ← Moderate, key integrations
   /------------\
  /    Unit      \ ← Many, fast, isolated

Coverage Targets

• Unit tests: > 80% line coverage
• Integration: Key API paths covered
• E2E: Critical user journeys

Test Design Patterns

Arrange-Act-Assert (AAA)

// Arrange: Setup preconditions
const user = createTestUser();

// Act: Execute behavior
const result = await login(user);

// Assert: Verify outcome
expect(result.success).toBe(true);

Test Characteristics

• Isolated: No shared state between tests
• Deterministic: Same result every run
• Fast: Quick feedback loop
• Readable: Self-documenting names

Definition of Done

Feature is complete when:

• All tests passing (unit, integration, E2E)
• Code meets style guides
• No console errors or unhandled exceptions
• API changes documented
• Performance budgets met

Performance Engineering

Systematic Approach

1. Baseline: Measure before optimizing
2. Identify: Profile to find bottlenecks
3. Budget: Set clear SLOs
4. Optimize: Implement improvements
5. Validate: Measure impact
6. Monitor: Continuous production tracking

Key Metrics

Performance Checklist

• Established performance baselines
• Load testing simulates realistic traffic
• Database queries optimized
• Caching strategy implemented
• CDN configured for static assets
• Monitoring dashboards in place
• Alerting on SLO breaches

Test Automation

Framework Selection

CI/CD Integration

# Pipeline stages
stages:
  - lint      # Fast feedback
  - unit      # Parallel execution
  - build     # Artifact creation
  - integration  # Service testing
  - e2e       # Critical paths
  - performance  # Load testing (optional)

Test Data Management

• Use factories/fixtures for consistent data
• Isolate test data from production
• Clean up after tests
• Consider data masking for sensitive info

Quality Metrics

Deliverables

• Test Strategy Document: Scope, objectives, methodology
• Test Cases: Step-by-step with expected results
• Automated Test Suite: Maintainable, organized tests
• CI Pipeline Config: Automated quality gates
• Coverage Reports: Visibility into tested code
• Performance Dashboards: Real-time metrics
• Bug Reports: Clear reproduction steps, severity

Anti-Patterns to Avoid

• Testing implementation details instead of behavior
• Flaky tests that pass/fail randomly
• Slow test suites blocking development
• Missing edge case coverage
• Manual-only regression testing
• No performance testing until production issues

Quick Reference

Common Issues