AI-Powered Test Generation and Coverage Prediction Training Course

Basics of AI-Powered Test Engineering

• Contemporary testing challenges and the role of AI
• Principles and terminology of generative testing
• Machine learning models applied to automated test creation

Converting Requirements and Code into AI-Created Tests

• Deriving intent from requirements and user stories
• Leveraging language models to produce structured test cases
• Guaranteeing determinism and reproducibility in AI-generated tests

Automated Unit Test Creation

• Generating unit tests based on source code context
• Creating input permutations and edge cases
• Integrating generated tests with standard unit testing frameworks

AI-Supported Integration and End-to-End Test Development

• Correlating system behavior with test flows
• Building integration paths through AI-driven analysis
• Striking a balance between human oversight and automated generation

Coverage Forecasting and Risk Modeling

• Employing ML models to spot under-tested code sections
• Anticipating high-risk areas based on historical failures
• Prioritizing tests using coverage and risk forecasts

Implementing AI-Based Test Intelligence in CI/CD

• Inserting AI analysis steps into pipelines
• Initiating dynamic test selection based on risk scores
• Sustaining a feedback loop for continuously refined predictions

Verification, Governance, and Quality Assurance

• Assessing the reliability of AI-generated tests
• Managing bias and preventing false positives
• Setting up guardrails for production deployment

Expanding AI-Driven Test Generation Across Teams

• Adoption strategies for QA and DevOps organizations
• Standardizing workflows and documentation
• Promoting continuous improvement through metrics and insights

Wrap-up and Future Actions