AI Code Smell Detection: Finding Problems in Test Automation with ML
Find test anti-patterns with AI: duplicate code, poor assertions, maintainability issues, refactoring suggestions
Testing LLMs, validating non-deterministic AI outputs, and ensuring quality in machine learning systems
Find test anti-patterns with AI: duplicate code, poor assertions, maintainability issues, refactoring suggestions
AI test infrastructure: auto-scaling, resource optimization, 40-60% cost reduction, predictive provisioning. Tools: AWS, GCP, Harness.io, Datadog
Automate bug prioritization: severity prediction, duplicate detection, assignment suggestions, SLA optimization
Ethical AI testing: fairness metrics, demographic parity, dataset bias, mitigation strategies, tools
Applitools Eyes, Percy by BrowserStack, AI-powered visual regression. How to automate UI verification with artificial intelligence and avoid false positives
Will AI replace QA engineers by 2030? Skills evolution, new roles, adaptation strategies, market analysis
Detect performance issues with AI: baseline learning, anomaly detection, trend analysis, alert optimization
AI test metrics: trend prediction, anomaly detection, automated insights, release readiness prediction. Tools: scikit-learn, GPT-4, Plotly dashboards