Quality & Validation Patterns

Quality assurance and validation patterns for AI-assisted development.

Core Patterns

Quality Frameworks

Code Quality

  • Static analysis integration
  • Linting and formatting
  • Complexity metrics
  • Technical debt tracking

Testing Strategies

  • Unit testing patterns
  • Integration testing
  • End-to-end testing
  • Performance testing

Validation Approaches

  • Output verification
  • Consistency checking
  • Regression testing
  • A/B testing

Risk Management

Risk Categories

  • Technical risks
  • Security risks
  • Operational risks
  • Business risks

Mitigation Strategies

  • Risk assessment matrices
  • Mitigation planning
  • Contingency procedures
  • Regular reviews

Quality Metrics

Development Metrics

  • Code coverage
  • Bug density
  • Cycle time
  • Lead time

AI-Specific Metrics

  • Accuracy rates
  • False positive/negative rates
  • Response time
  • Token efficiency

Continuous Improvement

  • Retrospectives
  • Metric tracking
  • Process refinement
  • Tool optimization