Sharing Data and Models in Software Engineering

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9780124172951: Sharing Data and Models in Software Engineering

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

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From the Back Cover:

Data science is about conversations, not just conclusions. The most useful results are those that are shared, discussed, debated, and used to guide further analysis. If results are not shared, they can be quickly forgotten. This book is about sharing ideas and how data mining can help that sharing. The book focuses on software engineering, but the methods it discusses apply to many domains.

Although sharing can provide increased insight, sharing ideas is not a simple matter. The bad news is that, usually, ideas are shared very badly. The good news is that recent research now allows us to offer guidance on how to use data miners to better share lessons learned from data.

All of the data required to reproduce, improve, or even refute the conclusions of this book is available in the PROMISE repository at http://openscience.us/repo or in the ISBSG repository at http://www.isbsg.org. In addition, the tools used in the book are based on open-sourced toolkits that are freely available for download. The authors sincerely hope that you will use these data and tools to evaluate, extend, and improve upon the results of this book.

About the Author:

Tim Menzies, Full Professor, CS, NC State and a former software research chair at NASA. He has published 200+ publications, many in the area of software analytics. He is an editorial board member (1) IEEE Trans on SE; (2) Automated Software Engineering journal; (3) Empirical Software Engineering Journal. His research includes artificial intelligence, data mining and search-based software engineering. He is best known for his work on the PROMISE open source repository of data for reusable software engineering experiments.

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.

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Book Description ELSEVIER SCIENCE TECHNOLOGY, United States, 2014. Paperback. Book Condition: New. Language: English . This book usually ship within 10-15 business days and we will endeavor to dispatch orders quicker than this where possible. Brand New Book. 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. Bookseller Inventory # EOD9780124172951

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Book Description ELSEVIER SCIENCE TECHNOLOGY, United States, 2014. Paperback. Book Condition: New. Language: English . Brand New Book. 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. Bookseller Inventory # AAZ9780124172951

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Book Description Book Condition: New. Publisher/Verlag: Morgan Kaufmann | 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 engineeringExplains how to start a project of data science for software engineering as well as how to identify and avoid likely pitfallsProvides a wide range of useful qualitative and quantitative principles ranging from very simple to cutting edge researchAddresses 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 | IntroductionData Science 101 Cross company data: Friend or Foe?Pruning: Relevancy is the Removal of Irrelevancy Easy Path: Smarter DesignInstance Weighting: How not to elaborate on analogies Privacy: Data in Disguise Stability: How to find a silver-bullet model?Complexity: How to ensemble multiple models? | Format: Paperback | Language/Sprache: english | 32 gr | 233x187x16 mm | 406 pp. Bookseller Inventory # K9780124172951

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Book Description Elsevier Science & Technology. Paperback. Book Condition: new. BRAND NEW, Sharing Data and Models in Software Engineering, Tim Menzies, Ekrem Kocaguneli, Burak Turhan, Leandro Minku, Fayola Peters, 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. Bookseller Inventory # B9780124172951

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