1

Moving beyond coverage metrics: How to measure the true ROI of AI in testing, and the economic case for driving the cost of testing toward zero at scale.

2

Using AI/ML to analyze code complexity and assign risk scores to new changes, allowing you to prioritize and select only the most critical tests.

3

Deep dive into self-healing scripts: how AI algorithms detect UI/DOM changes and automatically update element locators to eliminate maintenance toil.

4

Integrating AI as an intelligent quality gate within the pipeline, using anomaly detection to autonomously halt releases before production issues occur.