AI-Driven Software Testing explores how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing quality engineering (QE), making testing more intelligent, efficient, and adaptive.

The book begins by examining the critical role of QE in modern software development and the paradigm shift introduced by AI/ML. It traces the evolution of software testing, from manual approaches to AI-powered automation, highlighting key innovations that enhance accuracy, speed, and scalability. Readers will gain a deep understanding of quality engineering in the age of AI, comparing traditional and AI-driven testing methodologies to uncover their advantages and challenges. Moving into practical applications, the book delves into AI-enhanced test planning, execution, and defect management. It explores AI-driven test case development, intelligent test environments, and real-time monitoring techniques that streamline the testing lifecycle. Additionally, it covers AI's impact on continuous integration and delivery (CI/CD), predictive analytics for failure prevention, and strategies for scaling AI-driven testing across cloud platforms. Finally, it looks ahead to the future of AI in software testing, discussing emerging trends, ethical considerations, and the evolving role of QE professionals in an AI-first world.

With real-world case studies and actionable insights, AI-Driven Software Testing is an essential guide for QE engineers, developers, and tech leaders looking to harness AI for smarter, faster, and more reliable software testing.

What you will learn:

• What are the key principles of AI/ML-driven quality engineering

• What is intelligent test case generation and adaptive test automation

• Explore predictive analytics for defect prevention and risk assessment

• Understand integration of AI/ML tools in CI/CD pipelines

Who this book is for: Quality Engineers looking to enhance software testing with AI-driven techniques. Data Scientists exploring AI applications in software quality assurance and engineering. Software Developers Engineers seeking to integrate AI/ML into testing and automation workflows.

Explore how AI/ML transforms software testing, boosting accuracy, speed, and scalability Utilize AI/ML for instant defect detection, root cause analysis, and proactive issue resolution Learn to optimize test environments, CI/CD pipelines, and predictive analytics for AI-driven

Autorentext

Srinivasa Rao Bittla is a seasoned technology leader with over 20 years of expertise in AI/ML, performance engineering, and quality assurance. Currently at a multinational company, he drives AI-driven innovation, large-scale performance benchmarking, and automation frameworks that enhance scalability and system reliability. Previously, as a QE Manager at LogMeIn/Citrix, he developed cutting-edge performance testing tools and optimized CI/CD pipelines. Srini has also founded and led teams at Zest Bittla IT Solutions, mentoring over 10,000 professionals and transforming enterprise QE processes. A thought leader and IEEE Senior, Sigm-Xi Full Membership, Forbes Tech Council Contributor, he has delivered keynotes at AI testing conferences and contributed to peer-reviewed research. His work in AI-enabled predictive analytics and blockchain security has led to multiple patents. Recognized with awards like the Adobe Team Excellence Award and Titan Gold Award, Srini continues to shape the future of AI in software engineering. He is based in San Jose, US.

Srinivasa is also the author of The Last Invention: How Artificial Superintelligence Will Redefine Life , an Amazon-published book that explores the ethical, societal, and technological implications of Artificial Superintelligence (ASI). The book has been featured in academic discussions and has helped shape conversations around the future of AI in governance, education, and human evolution. His writing reflects a deep commitment to not only advancing AI in practical domains like software testing but also understanding its long-term transformative impact on humanity.

Klappentext

AI-Driven Software Testing explores how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing quality engineering (QE), making testing more intelligent, efficient, and adaptive. The book begins by examining the critical role of QE in modern software development and the paradigm shift introduced by AI/ML. It traces the evolution of software testing, from manual approaches to AI-powered automation, highlighting key innovations that enhance accuracy, speed, and scalability. Readers will gain a deep understanding of quality engineering in the age of AI, comparing traditional and AI-driven testing methodologies to uncover their advantages and challenges. Moving into practical applications, the book delves into AI-enhanced test planning, execution, and defect management. It explores AI-driven test case development, intelligent test environments, and real-time monitoring techniques that streamline the testing lifecycle. Additionally, it covers AI’s impact on continuous integration and delivery (CI/CD), predictive analytics for failure prevention, and strategies for scaling AI-driven testing across cloud platforms. Finally, it looks ahead to the future of AI in software testing, discussing emerging trends, ethical considerations, and the evolving role of QE professionals in an AI-first world. With real-world case studies and actionable insights, AI-Driven Software Testing is an essential guide for QE engineers, developers, and tech leaders looking to harness AI for smarter, faster, and more reliable software testing. What you will learn: • What are the key principles of AI/ML-driven quality engineering • What is intelligent test case generation and adaptive test automation • Explore predictive analytics for defect prevention and risk assessment • Understand integration of AI/ML tools in CI/CD pipelines Who this book is for: Quality Engineers looking to enhance software testing with AI-driven techniques. Data Scientists exploring AI applications in software quality assurance and engineering. Software Developers – Engineers seeking to integrate AI/ML into testing and automation workflows.

Inhalt

Part 1.- Chapter 1: The Role of AI and ML in Modern Software Testing.- Chapter 2: Software Testing from Manual to AI-Driven Automation.- Chapter 3: Quality Engineering in the Age of AI.- Chapter 4: Comparing Traditional and AI-Driven Testing.- Chapter 5: SDLC vs STLC Understanding the Basics.- Chapter 6: The Testing Pyramid in Traditional and AI-Driven Testing.- Part 2.- Chapter 7: Revolutionizing Test Planning and Execution with AI/ML.- Chapter 8: Intelligent Test Case Development with AI/ML.- Chapter 9: AI/ML-Driven Test Setup and Management.- Chapter 10: AI/ML in Smart Defect Management and Resolution.- Chapter 11: Test Closure with AI/ML Reporting and Continuous Feedback.- Chapter 12: Eliminating Testing Gaps with AI/ML Precision.- Part 3.- Chapter 13: Scaling Software Testing with AI/ML.- Chapter 14: Enhancing CI/CD Pipelines with AI/ML Driven Testing.- Chapter 15: AI/ML for Real-Time Test Execution Monitoring.- Chapter 16: Predicting Failures with AI/ML Analytics.- Chapter 17: The Future of QE with AI-Driven Testing.- Chapter 18. Next Steps to Implementing AI-Driven QE.