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Perspectives on data science for software engineering /
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. --
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, MA :
Morgan Kaufmann is an imprint of Elsevier,
2016.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 050 | 4 | |a QA76.758 |b .P47 2016 | |
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| 082 | 0 | 4 | |a 005.1 |2 23 |
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| 245 | 0 | 0 | |a Perspectives on data science for software engineering / |c edited by Tim Menzies, Laurie Williams, Thomas Zimmermann. |
| 264 | 1 | |a Cambridge, MA : |b Morgan Kaufmann is an imprint of Elsevier, |c 2016. | |
| 300 | |a 1 online resource (xxix, 378 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from PDF title page (ScienceDirect, viewed Aug. 1, 2016). | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 504 | |a Includes bibliographical references. | ||
| 520 | |a 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. -- |c Edited summary from book. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Software engineering. | |
| 650 | 6 | |a Génie logiciel. | |
| 650 | 7 | |a COMPUTERS / General. |2 bisacsh | |
| 650 | 7 | |a Software engineering |2 fast | |
| 700 | 1 | |a Menzies, Tim, |e editor. | |
| 700 | 1 | |a Williams, Laurie, |d 1962- |e editor. | |
| 700 | 1 | |a Zimmermann, Thomas, |e editor. | |
| 776 | 0 | 8 | |i Print version: |z 0128042060 |z 9780128042069 |w (OCoLC)926742865 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9780128042618/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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