Software Testing with Generative AI
Engineering Reliable Quality Systems in the Age of Probabilistic Software
Instructional overview
This book is also a teaching resource: it is written to help students and professionals reason from first principles about AI-enabled quality, uncertainty, and engineering responsibility.
Software testing is entering a new era.
Generative AI is no longer a futuristic experiment—it is already shaping how test cases are created, how risks are analyzed, how failures are diagnosed, and how quality decisions are made. Yet most existing guidance treats generative AI as a productivity shortcut or a tool to "speed things up," without addressing the deeper engineering implications.
In an academic setting, I would use this text to support modules on AI testing, software quality governance, risk-based thinking, and the shift from deterministic to probabilistic systems.
This book takes a different approach.
Testing with Generative AI is a rigorous, engineering-focused exploration of how generative models fundamentally change the nature of software testing—and what professionals must do to use them responsibly, safely, and effectively.
Rather than focusing on tools, certifications, or surface-level techniques, this book treats generative AI as a probabilistic software system that must be architected, constrained, validated, and governed with the same discipline applied to production systems.
Probabilistic systems demand stronger engineering discipline, not weaker standards.
