Sharing Data and Models in Software Engineering
Language: English
Published by Elsevier Science, 2014
- Softcover
- New

Condition: New
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Sharing Data and Models in Software Engineering | Tim Menzies (u. a.) | Taschenbuch | Englisch | 2014 | Elsevier Science | EAN 9780124172951 | Verantwortliche Person für die EU: Elsevier B.V., Radarweg 29, 1043 NX AMSTERDAM, NIEDERLANDE, nlinfo[at]elsevier[dot]com | Anbieter: preigu.
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- Title
- Sharing Data and Models in Software Engineering
- Author
- Tim Menzies (u. a.)
- Publisher
- Elsevier Science
- Publication year
- 2014
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0124172954
- ISBN 13
- 9780124172951
- Item weight
- 839 grams
- Dimensions
- 231 x 186 x 20 mm
- Seller catalogs
- Bücher
Data Science for Software Engineering: Sharing Data and Models presents guidance and procedures for reusing data and models between projects to produce results that are useful and relevant. Starting with a background section of practical lessons and warnings for beginner data scientists for software engineering, this edited volume proceeds to identify critical questions of contemporary software engineering related to data and models. Learn how to adapt data from other organizations to local problems, mine privatized data, prune spurious information, simplify complex results, how to update models for new platforms, and more. Chapters share largely applicable experimental results discussed with the blend of practitioner focused domain expertise, with commentary that highlights the methods that are most useful, and applicable to the widest range of projects. Each chapter is written by a prominent expert and offers a state-of-the-art solution to an identified problem facing data scientists in software engineering. Throughout, the editors share best practices collected from their experience training software engineering students and practitioners to master data science, and highlight the methods that are most useful, and applicable to the widest range of projects.
- Shares the specific experience of leading researchers and techniques developed to handle data problems in the realm of software engineering
- Explains how to start a project of data science for software engineering as well as how to identify and avoid likely pitfalls
- Provides a wide range of useful qualitative and quantitative principles ranging from very simple to cutting edge research
- Addresses current challenges with software engineering data such as lack of local data, access issues due to data privacy, increasing data quality via cleaning of spurious chunks in data
"Synopsis" may belong to another edition of this title.
About the Author
Ekrem Kocaguneli received his Ph.D. from the Lane Department of Computer Science and Electrical Engineering, West Virginia University. His research focuses on empirical software engineering, data/model problems associated with software estimation and tackling them with smarter machine learning algorithms.
Burak Turhan is a Professor of Software Engineering, University of Oulu, Finland. His research interests include empirical studies of software engineering on software quality, defect prediction, and cost estimation, as well as data mining for software engineering.
Leandro L. Minku is a Research Fellow II at the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, the University of Birmingham (UK). His research focuses on software prediction models, and he is the co-author of the first approach able to improve the performance of software predictors based on cross-company data over single-company data by taking into account the changeability of software prediction tasks' environments.
Fayola Peters is a PostDoctoral Researcher at LERO, the Irish Software Engineering Research Center, University of Limerick, Ireland. Along with Mark Grechanik, she is the author of one of the two known algorithms (presented at ICSE’12) that can privatize algorithms while still preserving the data mining properties of that data.
"About the title" may belong to another edition of this title.
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