04447cam a2200493Ii 4500 ocn953844182 OCoLC 20190328114815.0 m o d cr cnu---unuuu 160721s2016 maua ob 000 0 eng d YDXCP eng rda pn YDXCP OPELS UIU EBLCP N$T UMI IDEBK OCLCQ N$T OCLCF UPM TOH STF COO NAM DEBBG S9I OCLCQ 953849331 957279026 9780128042618 (electronic bk.) 0128042613 (electronic bk.) 0128042060 9780128042069 (OCoLC)953844182 (OCoLC)953849331 (OCoLC)957279026 QA76.758 .P47 2016 COM 051000 bisacsh 005.1 23 Perspectives on data science for software engineering / [electronic resource] edited by Tim Menzies, Laurie Williams, Thomas Zimmermann. Cambridge, MA : Morgan Kaufmann is an imprint of Elsevier, 2016. 1 online resource (xxix, 378 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from PDF title page (ScienceDirect, viewed Aug. 1, 2016). 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 Linguistics�� 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. Includes bibliographical references. Presenting the best practices of seasoned data miners in software engineering, this book offers unique insights into the wisdom of the community{OCLCbr#92}s leaders gathered to share hard-won lessons from the trenches. -- Edited summary from book. COMPUTERS / General. bisacsh Software engineering. Software engineering. fast (OCoLC)fst01124185 Electronic books. Menzies, Tim, editor. Williams, Laurie, 1962- editor. Zimmermann, Thomas, editor. Print version: 0128042060 9780128042069 (OCoLC)926742865 ScienceDirect http://www.sciencedirect.com/science/book/9780128042069 247381 247381