| Description |
1 online resource (xxix, 378 pages) : illustrations |
| Physical Medium |
polychrome |
| Description |
text file |
| Contents |
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. |
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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. |
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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. |
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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. |
| Bibliography |
Includes bibliographical references. |
| Summary |
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. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Software engineering.
|
|
Software engineering. |
|
COMPUTERS / General. |
| Genre/Form |
Electronic books.
|
| Added Author |
Menzies, Tim, editor.
|
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Williams, Laurie, 1962- editor.
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Zimmermann, Thomas, 1974- editor.
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| Other Form: |
Print version: 0128042060 9780128042069 (OCoLC)926742865 |
| ISBN |
9780128042618 (electronic book) |
|
0128042613 (electronic book) |
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0128042060 |
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9780128042069 |
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