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The Art and Science of Analyzing Software Data
Chapter 1 Past, Present, and Future of Analyzing Software Data
Part 1 Tutorial-Techniques
Chapter 2 Mining Patterns and Violations Using Concepts Analysis
Chapter 3 Analyzing Text in Software Projects
Chapter 4 Synthesizing Knowledge from Software Development Artifacts
Chapter 5 A Practical Guide to Analyzing IDE Usage Data
Chapter 6 Latent Dirichlet Allocation: Extracting Topics from Software Engineering Data
Chapter 7 Tools and Techniques for Analyzing Product and Process Data
Part 2 Data/Problem Focussed
Chapter 8 Analyzing Security Data
Chapter 9 A Mixed Methods Approach to Mining Code Review Data: Examples and a Study of Multicommit Reviews and Pull Requests
Chapter 10 Mining Android Apps for Anomalies
Chapter 11 Change Coupling Between Software Artifacts: Learning from Past Changes
Part 3 Stories from the Trenches
Chapter 12 Applying Software Data Analysis in Industry Contexts: When Research Meets Reality
Chapter 13 Using Data to Make Decisions in Software Engineering Providing a Method to our Madness
Chapter 14 Community Data for OSS Adoption Risk Management
Chapter 15 Assessing the States of Software in a Large Enterprise: A 12-Year Restrospective
Chapter 16 Lessons Learned from Software Analytics in Practice
Part 4 Advanced Topics
Chapter 17 Code Comment Analysis for Improving Software Quality
Chapter 18 Mining Software Logs for Goal-Driven Root Cause Analysis
Chapter 19 Analytical Product Release Planning
Part 5 Data Analysis at Scale (Big Data)
Chapter 20 Boa: An Enabling Language and Infrastructure for Ultra-Large-Scale MSR Studies
Chapter 21 Scalable Parallelization of Specification Mining Using Distributed Computing
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