Coverart for item
The Resource Perspectives on data science for software engineering, edited by Tim Menzies, Laurie Williams, Thomas Zimmermann, (electronic book)

Perspectives on data science for software engineering, edited by Tim Menzies, Laurie Williams, Thomas Zimmermann, (electronic book)

Label
Perspectives on data science for software engineering
Title
Perspectives on data science for software engineering
Statement of responsibility
edited by Tim Menzies, Laurie Williams, Thomas Zimmermann
Contributor
Editor
Subject
Language
eng
Cataloging source
YDXCP
Dewey number
005.1
Index
no index present
LC call number
QA76.758
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorDate
1962-
http://library.link/vocab/relatedWorkOrContributorName
  • Menzies, Tim
  • Williams, Laurie
  • Zimmermann, Thomas
http://library.link/vocab/subjectName
Software engineering
Label
Perspectives on data science for software engineering, edited by Tim Menzies, Laurie Williams, Thomas Zimmermann, (electronic book)
Instantiates
Publication
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
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 Ǹ̀atural 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
Control code
SCIDI953844182
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9780128042618
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
Label
Perspectives on data science for software engineering, edited by Tim Menzies, Laurie Williams, Thomas Zimmermann, (electronic book)
Publication
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
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 Ǹ̀atural 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
Control code
SCIDI953844182
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9780128042618
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote

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