The Art and Science of Analyzing Software Data
| Main Authors: | BIRD, Christian, MENZIES, Tim, ZIMMERMANN, Thomas |
|---|---|
| Format: | Book xxiii, 660p., Index. 19 x 23.5 cm |
| Bahasa: | eng |
| Terbitan: |
Morgan Kaufmann
, 2015
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| Subjects: | |
| Online Access: |
http://elib.polban.ac.id//index.php?p=show_detail&id=17982 http://elib.polban.ac.id//lib/minigalnano/createthumb.php?filename=images/docs/The_Art_and_Science_of_Analyzing_Software_Data_BIRD.jpg.jpg&width=200 |
Daftar Isi:
- Chapter 1 Past, Present, and Future of Analyzing Software DataPart 1 Tutorial-TechniquesChapter 2 Mining Patterns and Violations Using Concepts AnalysisChapter 3 Analyzing Text in Software ProjectsChapter 4 Synthesizing Knowledge from Software Development ArtifactsChapter 5 A Practical Guide to Analyzing IDE Usage DataChapter 6 Latent Dirichlet Allocation: Extracting Topics from Software Engineering DataChapter 7 Tools and Techniques for Analyzing Product and Process DataPart 2 Data/Problem FocussedChapter 8 Analyzing Security DataChapter 9 A Mixed Methods Approach to Mining Code Review Data: Examples and a Study of Multicommit Reviews and Pull RequestsChapter 10 Mining Android Apps for AnomaliesChapter 11 Change Coupling Between Software Artifacts: Learning from Past ChangesPart 3 Stories from the TrenchesChapter 12 Applying Software Data Analysis in Industry Contexts: When Research Meets RealityChapter 13 Using Data to Make Decisions in Software Engineering Providing a Method to our MadnessChapter 14 Community Data for OSS Adoption Risk ManagementChapter 15 Assessing the States of Software in a Large Enterprise: A 12-Year RestrospectiveChapter 16 Lessons Learned from Software Analytics in PracticePart 4 Advanced TopicsChapter 17 Code Comment Analysis for Improving Software QualityChapter 18 Mining Software Logs for Goal-Driven Root Cause AnalysisChapter 19 Analytical Product Release PlanningPart 5 Data Analysis at Scale (Big Data)Chapter 20 Boa: An Enabling Language and Infrastructure for Ultra-Large-Scale MSR StudiesChapter 21 Scalable Parallelization of Specification Mining Using Distributed Computing