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Advances in Machine Learning Applications in Software Engineering

Advances in Machine Learning Applications in Software Engineering

Copyright: © 2007 |Pages: 498
ISBN13: 9781591409410|ISBN10: 1591409411|EISBN13: 9781591409434|ISBN13 Softcover: 9781591409427
DOI: 10.4018/978-1-59140-941-0
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MLA

Zhang, Du, and Jeffery J.P. Tsai, editors. Advances in Machine Learning Applications in Software Engineering. IGI Global, 2007. https://doi.org/10.4018/978-1-59140-941-0

APA

Zhang, D. & Tsai, J. J. (Eds.). (2007). Advances in Machine Learning Applications in Software Engineering. IGI Global. https://doi.org/10.4018/978-1-59140-941-0

Chicago

Zhang, Du, and Jeffery J.P. Tsai, eds. Advances in Machine Learning Applications in Software Engineering. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59140-941-0

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Machine learning is the study of building computer programs that improve their performance through experience. To meet the challenge of developing and maintaining larger and complex software systems in a dynamic and changing environment, machine learning methods have been playing an increasingly important role in many software development and maintenance tasks.

Advances in Machine Learning Applications in Software Engineering provides analysis, characterization, and refinement of software engineering data in terms of machine learning methods. This book depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality. Advances in Machine Learning Applications in Software Engineering offers readers direction for future work in this emerging research field.

Table of Contents

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Front Materials
Title Page
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Copyright Page
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Preface
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Acknowledgments
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Chapters
Chapter 1
Data Analysis and Refinement
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Chapter 2
Applications of Software Development
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Predictive Models for Software Quality and Relevancy
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Chapter 4
State-of-the-Practice  (pages 264-264)
State-of-the-Practice
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Chapter 5
Areas of Future Work  (pages 318-318)
Areas of Future Work
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Back Materials
About the Editors
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About the Authors
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Index
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