AI-Powered Test Generation and Coverage Prediction Training Course

Core Concepts of AI-Driven Test Engineering

• Contemporary testing challenges and the impact of AI
• Principles and terminology of generative testing
• Machine learning models applied in 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
• Safeguarding determinism and reproducibility in AI-generated tests

Automated Unit Test Production

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

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

• Mapping system behaviour to test flows
• Constructing integration paths through AI-driven analysis
• Balancing human oversight with automated generation

Coverage Forecasting and Risk Modelling

• Employing ML models to spot under-tested code regions
• Predicting high-risk areas based on historical failure data
• Prioritising tests using coverage and risk predictions

Implementing AI-Based Test Intelligence in CI/CD

• Embedding AI analysis stages into pipelines
• Triggering dynamic test selection based on risk scores
• Maintaining a feedback loop for continuously refined predictions

Verification, Governance, and Quality Assurance

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

Scaling AI-Powered Test Generation Across Organisations

• Adoption strategies for QA and DevOps teams
• Standardising workflows and documentation
• Driving continuous improvement through metrics and insights

Conclusion and Future Directions