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 applied to automated test creation

Converting Requirements and Code into AI-Generated Tests

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

Automated Generation of Unit Tests

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

AI-Assisted Creation of Integration and End-to-End Tests

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

Coverage Prediction and Risk Modelling

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

Implementing AI-Based Test Intelligence in CI/CD

• Integrating AI analysis steps into pipelines
• Selecting dynamic tests based on risk scores
• Maintaining a feedback loop for continuously refined predictions

Validation, Governance, and Quality Assurance

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

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

Conclusion and Next Steps