Transition from reactive quality assurance to proactive quality engineering by embedding shared responsibility throughout the entire development lifecycle

Combine AI-generated tests with intelligent test selection to manage large regression suites and speed up feedback

Identify sources of unnecessary cognitive load and apply strategies to focus on meaningful analysis and exploration.

Learn how focusing on user value and trust gives you a clearer, more effective way to test data quality

Test system resilience by mapping failure paths and running small experiments that reveal what users experience when things fail

Apply a four-dimension framework to assess whether synthetic data can be trusted for performance testing.

Discover how diverse perspectives in testing help reveal hidden bugs and build software that works for more users.

Use these structured prompting techniques to improve the quality and usefulness of AI output in testing workflows

Understand why testing must evolve beyond deterministic checks to assess fairness, accountability, resilience and transparency in AI-driven systems

Avoid these mistakes to build automation that survives UI changes, validates outcomes properly, and provides useful feedback.

Identify blind spots created by AI-driven testing and prevent false confidence in testing outcomes

Explore why human connection still matters most for quality, collaboration, and sustainable teamwork in an AI era