Sharing data and models in software engineering. Menzies, T.; Kocaguneli, E.; Minku, L.; Peters, F.; and Turhan, B. Morgan Kaufmann, First edition, 2015.
Sharing data and models in software engineering [link]Paper  abstract   bibtex   
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.
@book{menzies2015sharing,
  abstract = {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.},
  added-at = {2015-09-17T22:50:27.000+0200},
  author = {Menzies, Tim and Kocaguneli, Ekrem and Minku, Leandro and Peters, Fayola and Turhan, Burak},
  biburl = {http://www.bibsonomy.org/bibtex/21a163b0bbe83f698f08c3bfda306118c/burak.turhan},
  edition = {First},
  interhash = {032eb6c9be57c7a684a40520c73f1383},
  intrahash = {1a163b0bbe83f698f08c3bfda306118c},
  isbn = {9780124173071 0124173071},
  keywords = {myown},
  publisher = {Morgan Kaufmann},
  refid = {896901265},
  timestamp = {2015-09-17T22:50:27.000+0200},
  title = {Sharing data and models in software engineering},
  url = {http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=932996},
  year = 2015
}
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