AI Copilot for Test Automation: GitHub Copilot, Amazon CodeWhisperer and the Future of QA
GitHub Copilot and CodeWhisperer for test automation: real examples, productivity gains, best practices
Testing LLMs, validating non-deterministic AI outputs, and ensuring quality in machine learning systems
GitHub Copilot and CodeWhisperer for test automation: real examples, productivity gains, best practices
Test AI on devices: resource constraints, latency requirements, model optimization, deployment testing
ML model experiments: statistical significance, online/offline evaluation, feature flags, rollout strategies
AI test data generation: GANs, VAEs, synthetic datasets, privacy compliance, edge case generation. Tools: Tonic, Gretel, SDV
AI security testing: ML fuzzing, automated pentesting, vulnerability prediction, threat modeling. Reduce false positives by 80%
How to use ChatGPT, GPT-4 and other LLMs for test data generation, test case creation, code review. Practical examples, risks and limitations
Understanding AI decisions: interpretability testing, LIME, SHAP, model transparency, regulatory compliance
AI-enhanced mutation testing: intelligent mutant generation, test effectiveness measurement, coverage gaps