AI-Powered Test Generation and Coverage Prediction Training Course

Foundations of AI-Driven Test Engineering

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

Converting Requirements and Code into AI-Generated Tests

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

Automated Unit Test Generation

• Generating unit tests from source code context
• Creating input permutations and edge cases
• Incorporating generated tests into popular unit testing frameworks

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

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

Coverage Forecasting and Risk Modeling

• Identifying under-tested code regions using ML models
• Predicting high-risk areas based on historical failure data
• Prioritizing tests utilizing coverage and risk predictions

Implementing AI-Based Test Intelligence in CI/CD

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

Validation, Governance, and Quality Assurance

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

Summary and Next Steps