Testing LLMs, validating non-deterministic AI outputs, and ensuring quality in machine learning systems
Test AI on devices: resource constraints, latency requirements, model optimization, deployment testing
Understanding AI decisions: interpretability testing, LIME, SHAP, model transparency, regulatory compliance
Identify unstable tests with ML: pattern analysis, failure prediction, root causes, stabilization strategies
AI-enhanced mutation testing: intelligent mutant generation, test effectiveness measurement, coverage gaps
Convert requirements to tests with NLP: user story parsing, test scenario generation, BDD automation
AI-driven test selection: risk prediction, test impact analysis, execution optimization, CI/CD integration
Master AI prompts for QA: effective queries for test generation, bug analysis, documentation, best practices
QA for quantum computing: probabilistic testing, qubit validation, simulation strategies, new paradigms