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Título Perspectives on data science for software engineering / edited by Tim Menzies, Laurie Williams, Thomas Zimmermann.

Publicación Cambridge, MA : Morgan Kaufmann is an imprint of Elsevier, 2016.
Descripción física 1 online resource (xxix, 378 pages) : illustrations
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Colección ScienceDirect 2015/16
Nota Online resource; title from PDF title page (ScienceDirect, viewed Aug. 1, 2016).
Contiene: Front Cover; Perspectives on Data Science for Software Engineering; Copyright; Contents; Contributors; Acknowledgments; Introduction; Perspectives on data science for software engineering; Why This Book?; About This Book; The Future; References; Software analytics and its application in practice; Six Perspectives of Software Analytics; Experiences in Putting Software Analytics into Practice; References; Seven principles of inductive software engineering: What we do is different; Different and Important; Principle #1: Humans Before Algorithms; Principle #2: Plan for Scale.
Principle #3: Get Early FeedbackPrinciple #4: Be Open Minded; Principle #5: Be smart with your learning; Principle #6: Live With the Data You Have; Principle #7: Develop a Broad Skill Set That Uses a Big Toolkit; References; The need for data analysis patterns (in software engineering); The Remedy Metaphor; Software Engineering Data; Needs of Data Analysis Patterns; Building Remedies for Data Analysis in Software Engineering Research; References; From software data to software theory: The path less traveled; Pathways of Software Repository Research; From Observation, to Theory, to Practice.
Dynamic Artifacts Are Here to StayAcknowledgments; References; Mobile app store analytics; Introduction; Understanding End Users; Conclusion; References; The naturalness of software*; Introduction; Transforming Software Practice; Porting and Translation; The ̀̀Natural Linguisticś́ of Code; Analysis and Tools; Assistive Technologies; Conclusion; References; Advances in release readiness; Predictive Test Metrics; Universal Release Criteria Model; Best Estimation Technique; Resource/Schedule/Content Model; Using Models in Release Management.
Research to Implementation: A Difficult (but Rewarding) JourneyHow to tame your online services; Background; Service Analysis Studio; Success Story; References; Measuring individual productivity; No Single and Simple Best Metric for Success/Productivity; Measure the Process, Not Just the Outcome; Allow for Measures to Evolve; Goodharts Law and the Effect of Measuring; How to Measure Individual Productivity?; References; Stack traces reveal attack surfaces; Another Use of Stack Traces?; Attack Surface Approximation; References; Visual analytics for software engineering data; References.
Bibliografía Includes bibliographical references.
Materia COMPUTERS / General.
Software engineering.
Software engineering. (OCoLC)fst01124185
Autor secundario Menzies, Tim, editor.
Williams, Laurie, 1962- editor.
Zimmermann, Thomas, editor.
OTRO SOPORTE Print version: 0128042060 9780128042069
ISBN 9780128042618 (electronic bk.)
0128042613 (electronic bk.)
CHVBK 403947987
CHBIS 010796357
DEBBG BV043894987