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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Yinglei Song, Doctor of Engineering, is a professor and postgraduate supervisor at Jiangsu University of Science and Technology, engaged in data-mining applications for automation; he serves as ASR and IACSIT director, sits on several journal editorial boards and IEEE conference committees, and researches image processing, adaptive control, etc.
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Paperback. Zustand: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9786209154355
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Paperback. Zustand: new. Paperback. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9786209154355
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Paperback. Zustand: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9786209154355
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Taschenbuch. Zustand: Neu. Software Testing based on Artificial Intelligence Technology | Yinglei Song | Taschenbuch | Englisch | 2025 | Scholars' Press | EAN 9786209154355 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 134327046
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