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

Resource Information

The instance Perspectives on data science for software engineering, edited by Tim Menzies, Laurie Williams, Thomas Zimmermann, (electronic book) represents a material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool. This resource is a combination of several types including: Instance, Electronic.

Image

http://covers.openlibrary.org/b/isbn/9780128042618-M.jpg?default=false

Label

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

Medium

electronic book

Statement of responsibility

edited by Tim Menzies, Laurie Williams, Thomas Zimmermann

Instantiates

• Perspectives on data science for software engineering

Publication

• Cambridge, MA, Morgan Kaufmann is an imprint of Elsevier, 2016

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

Record ID

b4035151

Reformatting quality

unknown

Sound

unknown sound

Specific material designation

remote