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. --
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, MA :
Morgan Kaufmann is an imprint of Elsevier,
2016.
|
| Schlagworte: | |
| Online-Zugang: | Volltext Volltext |
| Zusammenfassung: | 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. -- |
| Beschreibung: | 1 online resource (xxix, 378 pages) : illustrations |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9780128042618 0128042613 |
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| 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. | |
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| 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. 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. |
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| spelling | Perspectives on data science for software engineering / 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. Software engineering. http://id.loc.gov/authorities/subjects/sh87007398 Génie logiciel. COMPUTERS / General. bisacsh Software engineering fast Menzies, Tim, editor. http://id.loc.gov/authorities/names/no2003012478 Williams, Laurie, 1962- editor. https://id.oclc.org/worldcat/entity/E39PCjFY7J7Xmqt687pxC3YWwC http://id.loc.gov/authorities/names/n2002153248 Zimmermann, Thomas, editor. http://id.loc.gov/authorities/names/n80056019 Print version: 0128042060 9780128042069 (OCoLC)926742865 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1144641 Volltext FWS01 ZDB-4-EBA FWS_PDA_EBA https://www.sciencedirect.com/science/book/9780128042069 Volltext |
| spellingShingle | Perspectives on data science for software engineering / 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. Software engineering. http://id.loc.gov/authorities/subjects/sh87007398 Génie logiciel. COMPUTERS / General. bisacsh Software engineering fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh87007398 |
| title | Perspectives on data science for software engineering / |
| title_auth | Perspectives on data science for software engineering / |
| title_exact_search | Perspectives on data science for software engineering / |
| title_full | Perspectives on data science for software engineering / edited by Tim Menzies, Laurie Williams, Thomas Zimmermann. |
| title_fullStr | Perspectives on data science for software engineering / edited by Tim Menzies, Laurie Williams, Thomas Zimmermann. |
| title_full_unstemmed | Perspectives on data science for software engineering / edited by Tim Menzies, Laurie Williams, Thomas Zimmermann. |
| title_short | Perspectives on data science for software engineering / |
| title_sort | perspectives on data science for software engineering |
| topic | Software engineering. http://id.loc.gov/authorities/subjects/sh87007398 Génie logiciel. COMPUTERS / General. bisacsh Software engineering fast |
| topic_facet | Software engineering. Génie logiciel. COMPUTERS / General. Software engineering |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1144641 https://www.sciencedirect.com/science/book/9780128042069 |
| work_keys_str_mv | AT menziestim perspectivesondatascienceforsoftwareengineering AT williamslaurie perspectivesondatascienceforsoftwareengineering AT zimmermannthomas perspectivesondatascienceforsoftwareengineering |