AI-Enabled Testing & Quality Assurance
Examines how AI improves test coverage, identifies high-risk areas, and increases confidence in software quality.
Sample assessment questions for each level:
- Level -1: “Is there active resistance to using AI for testing or quality assurance?”
- Level 0: “Are AI testing tools used by individual QA engineers without standardization?”
- Level 1: “Has the team evaluated potential AI tools for enhancing test coverage or efficiency?”
- Level 2: “Are AI tools used for basic test data generation or simple test automation?”
- Level 3: “Are AI tools used to generate or prioritise test cases?”
- Level 4: “Does AI detect flaky tests, redundancies, or missing coverage?”
- Level 5: “Is regression risk analysis performed with AI before release?”
Key metrics to track:
- Test coverage expansion: Percentage increase in code coverage with AI-generated tests (this one will be interesting.. As we do not want a 100 percent unit test coverage or a million tests that take forever to run?)
- Defect prediction accuracy: Percentage of AI-identified high-risk areas that contain defects
- Testing efficiency: Reduction in QA time while maintaining or improving quality
- Test maintenance reduction: Percentage decrease in test maintenance burden with AI assistance
- Bug escape rate: Percentage reduction in production defects after AI testing implementation
