This book presents a comprehensive framework that revolutionizes software quality assurance by leveraging the power of artificial intelligence and machine learning. Through the innovative integration of ensemble learning techniques and intelligent optimization algorithms, this book offers practical solutions to two critical challenges: efficient test case generation and accurate code smell detection. The author masterfully combines theoretical foundations with real-world applications, introducing novel approaches such as combinatorial optimization with improved genetic algorithms and enhanced harmony search for regression testing. Whether you're a software engineer, quality assurance professional, or researcher, this book provides the tools and methodologies needed to dramatically improve testing efficiency while reducing costs. With detailed case studies, empirical evaluations, and clear implementation guidelines, readers will learn how to implement state-of-the-art testing strategies that adapt to the increasing demands of modern software development.

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