AI-Powered Test Generation and Coverage Prediction Training Course

Foundations of AI-Driven Test Engineering

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

Converting Requirements and Code into AI-Generated Tests

• Interpreting intent from requirements and user stories
• Employing language models to create structured test cases
• Safeguarding determinism and reproducibility in AI-generated tests

Automated Unit Test Generation

• Deriving unit tests from source code context
• Generating input variations and edge cases
• Integrating generated tests with standard unit testing frameworks

AI-Assisted Integration and End-to-End Test Creation

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

Coverage Prediction and Risk Modeling

• Using ML models to spot under-tested code segments
• Forecasting high-risk areas based on past failures
• Prioritizing tests using coverage and risk forecasts

Applying AI-Based Test Intelligence in CI/CD

• Incorporating AI analysis steps into pipelines
• Initiating dynamic test selection based on risk scores
• Maintaining a feedback loop for continuously enhancing predictions

Validation, Governance, and Quality Assurance

• Assessing the reliability of AI-generated tests
• Mitigating bias and preventing false positives
• Implementing safeguards for production use

Scaling AI-Powered Test Generation Across Teams

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

Summary and Next Steps