• The Art and Science of Analyzing Software Data (Paperback) 9780124115194
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The Art and Science of Analyzing Software Data

A comprehensive guide to the art and science of analyzing software data, with best practices generated by leading data scientists, collected from their experience training software engineering students and practitioners on how to master data science.

Christian Bird (Edited by), Tim Menzies (Edited by), Thomas Zimmermann (Edited by)

9780124115194, Elsevier Science

Paperback, published 27 August 2015

672 pages
23.4 x 19 x 4.1 cm, 1.4 kg

The Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science.

The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as co-change analysis, text analysis, topic analysis, and concept analysis, as well as advanced topics such as release planning and generation of source code comments. It includes stories from the trenches from expert data scientists illustrating how to apply data analysis in industry and open source, present results to stakeholders, and drive decisions.

  1. Past, Present, and Future of Analyzing Software Data

    2. Part 1 TUTORIAL-TECHNIQUES

  2. Mining Patterns and Violations Using Concept Analysis

  3. Analyzing Text in Software Projects

  4. Synthesizing Knowledge from Software Development Artifacts

  5. A Practical Guide to Analyzing IDE Usage Data

  6. Latent Dirichlet Allocation: Extracting Topics from Software Engineering Data

  7. Tools and Techniques for Analyzing Product and Process Data

    8. PART 2 DATA/PROBLEM FOCUSSED

  8. Analyzing Security Data

  9. A Mixed Methods Approach to Mining Code Review Data: Examples and a Study of Multicommit Reviews and Pull Requests

  10. Mining Android Apps for Anomalies

  11. Change Coupling Between Software Artifacts: Learning from Past Changes

    12. PART 3 STORIES FROM THE TRENCHES

  12. Applying Software Data Analysis in Industry Contexts: When Research Meets Reality

  13. Using Data to Make Decisions in Software Engineering:

  14. Providing a Method to our Madness

  15. Community Data for OSS Adoption Risk Management

  16. Assessing the State of Software in a Large Enterprise: A 12-Year Retrospective

  17. Lessons Learned from Software Analytics in Practice

    18. PART 4 ADVANCED TOPICS

  18. Code Comment Analysis for Improving Software Quality

  19. Mining Software Logs for Goal-Driven Root Cause Analysis

  20. Analytical Product Release Planning

    21. PART 5 DATA ANALYSIS AT SCALE (BIG DATA)

  21. Boa: An Enabling Language and Infrastructure for Ultra-Large-Scale MSR Studies

  22. Scalable Parallelization of Specification Mining Using Distributed Computing

Subject Areas: Systems analysis & design [UYD], Data mining [UNF], Software Engineering [UMZ]
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