Cantitate/Preț
Produs

The Art and Science of Analyzing Software Data

Editat de Christian Bird, Tim Menzies, Thomas Zimmermann
en Limba Engleză Paperback – 26 aug 2015
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.


  • Presents best practices, hints, and tips to analyze data and apply tools in data science projects
  • Presents research methods and case studies that have emerged over the past few years to furtherunderstanding of software data
  • Shares stories from the trenches of successful data science initiatives in industry
Citește tot Restrânge

Preț: 29940 lei

Preț vechi: 40754 lei
-27% Nou

Puncte Express: 449

Preț estimativ în valută:
5730 6045$ 4775£

Carte tipărită la comandă

Livrare economică 27 decembrie 24 - 10 ianuarie 25

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9780124115194
ISBN-10: 0124115195
Pagini: 672
Dimensiuni: 191 x 235 x 28 mm
Greutate: 1.41 kg
Editura: ELSEVIER SCIENCE

Public țintă

Practicing Software engineers, researchers and graduate software engineering students with an interest in data science.

Cuprins

  1. Past, Present, and Future of Analyzing Software DataPart 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 DataPART 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 ChangesPART 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 PracticePART 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 PlanningPART 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