AI-Powered Test Generation and Coverage Prediction Training Course

Foundations of AI-Driven Test Engineering

• Contemporary testing challenges and the impact of AI
• Core principles and terminology in generative testing
• ML models applied in automated test creation

Converting Requirements and Code into AI-Generated Tests

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

Automated Unit Test Generation

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

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

• Aligning system behavior with test flows
• Developing integration paths via AI-driven analysis
• Balancing human oversight with automated generation

Coverage Prediction & Risk Modeling

• Identifying under-tested code regions using ML models
• Forecasting high-risk areas based on historical failures
• Prioritizing tests using coverage and risk predictions

Applying AI-Based Test Intelligence in CI/CD

• Integrating AI analysis steps into pipelines
• Triggering dynamic test selection based on risk scores
• Maintaining a feedback loop for continuously improved predictions

Validation, Governance & Quality Assurance

• Evaluating the reliability of AI-generated tests
• Managing bias and mitigating false positives
• Establishing guardrails for production deployment

Scaling AI-Powered Test Generation Across Teams

• Adoption strategies for QA and DevOps organizations
• Standardizing workflows and documentation
• Driving continuous improvement with metrics and insights

Summary & Next Steps