Testing LLMs, validating non-deterministic AI outputs, and ensuring quality in machine learning systems
ML model experiments: statistical significance, online/offline evaluation, feature flags, rollout strategies
Find test anti-patterns with AI: duplicate code, poor assertions, maintainability issues, refactoring suggestions
GitHub Copilot and CodeWhisperer for test automation: real examples, productivity gains, best practices
Detect performance issues with AI: baseline learning, anomaly detection, trend analysis, alert optimization
Smart log analysis: anomaly detection, pattern recognition, root cause analysis, alert reduction, tools
Generate test data with AI: synthetic datasets, privacy compliance, edge cases, tools, best practices
From screenshots to reports: automatic documentation generation, video analysis, step extraction, insights
AI manages test infrastructure: auto-scaling, resource optimization, cost reduction, predictive provisioning