The Art and Science of Analyzing Software Data

<ol> <li>Past, Present, and Future of Analyzing Software Data</li> <p>Part 1 TUTORIAL-TECHNIQUES</p> <p> <li>Mining Patterns and Violations Using Concept Analysis</li> <p> <li>Analyzing Text in Software Projects</li> <p> <li>Synthesizing Knowledge from Software Development Artifacts</li> <p> <li>A Practical Guide to Analyzing IDE Usage Data</li> <p> <li>Latent Dirichlet Allocation: Extracting Topics from Software Engineering Data</li> <p> <li>Tools and Techniques for Analyzing Product and Process Data</li> <p>PART 2 DATA/PROBLEM FOCUSSED</p> <p> <li>Analyzing Security Data</li> <p> <li>A Mixed Methods Approach to Mining Code Review Data: Examples and a Study of Multicommit Reviews and Pull Requests</li> <p> <li>Mining Android Apps for Anomalies</li> <p> <li>Change Coupling Between Software Artifacts: Learning from Past Changes</li> <p>PART 3 STORIES FROM THE TRENCHES</p> <p> <li>Applying Software Data Analysis in Industry Contexts: When Research Meets Reality</li> <p> <li>Using Data to Make Decisions in Software Engineering:</li> <p> <li>Providing a Method to our Madness</li> <p> <li>Community Data for OSS Adoption Risk Management</li> <p> <li>Assessing the State of Software in a Large Enterprise: A 12-Year Retrospective</li> <p> <li>Lessons Learned from Software Analytics in Practice</li> <p>PART 4 ADVANCED TOPICS</p> <p> <li>Code Comment Analysis for Improving Software Quality</li> <p> <li>Mining Software Logs for Goal-Driven Root Cause Analysis</li> <p> <li>Analytical Product Release Planning</li> <p>PART 5 DATA ANALYSIS AT SCALE (BIG DATA)</p> <p> <li>Boa: An Enabling Language and Infrastructure for Ultra-Large-Scale MSR Studies</li> <p> <li>Scalable Parallelization of Specification Mining Using Distributed Computing</li> </ol>