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

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

Baseline: Measure before optimizing
Identify: Profile to find bottlenecks
Budget: Set clear SLOs
Optimize: Implement improvements
Validate: Measure impact
Monitor: Continuous production tracking

Key Metrics

| Layer | Metrics | |-------|---------| | Frontend | LCP, INP, CLS, TTFB | | API | Response time, throughput, error rate | | Database | Query time, connections, locks | | Infrastructure | CPU, memory, I/O, network |

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

| Type | Recommended Tools | |------|-------------------| | Unit | Jest, Pytest, JUnit | | Integration | Testcontainers, SuperTest | | E2E | Playwright, Cypress | | Load | k6, Locust, Gatling | | Coverage | Istanbul, JaCoCo |

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

| Metric | Target | Purpose | |--------|--------|---------| | Test Coverage | > 80% | Code confidence | | Test Pass Rate | > 98% | Stability | | Flaky Test Rate | < 2% | Reliability | | Build Time | < 10 min | Fast feedback | | MTTR | < 1 hour | Recovery speed |

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