Abstract:
Software engineering data (such as code bases, execution traces, historical code changes, mailing lists, and bug databases) contains a wealth of information about a project’s status, progress, and evolution. Using well established data mining techniques, practitioners and researchers can explore the potential of this valuable data in order to better manage their projects and to produce higher quality software systems that are delivered on time and within budget.
This tutorial presents the latest research in mining Software Engineering (SE) data, discusses challenges associated with mining SE data, highlights SE data mining success stories, and outlines future research directions. Attendees will acquire the knowledge and skills needed to perform research or conduct practice in the field and to integrate data mining techniques in their own research or practice.
The tutorial has been very well attended and received in previous offerings at ICSE. More information including tutorial slides can be found on the tutorial website: https://sites.google.com/site/asergrp/dmse/.
Presenters:
Ahmed E. Hassan is the NSERC/RIM Industrial Research Chair in Software Engineering at the School of Computing in Queen's University. He spearheaded the organization and creation of the Mining Software Repositories (MSR) conference (http://msr.uwaterloo.ca/) and its research community He currently chairs the steering committee for the MSR conference. He co-edited special issues of the IEEE Transaction on Software Engineering and the Journal of Empirical Software Engineering on the MSR topic. Early work by Dr. Hassan's team is already integrated into products used by millions of users worldwide. His industrial experience includes helping architect the Blackberry wireless platform at RIM, and working for IBM Research at the Almaden Research Center. He is the named inventor of patents at several jurisdictions around the world including the United States, Europe, India, Canada, and Japan. [Homepage: http:// www.cs.queensu.ca/~ahmed/]
Tao Xie is an Assistant Professor in the Department of Computer Science at North Carolina State University since 2005, when he received his Ph.D. in Computer Science from the University of Washington at Seattle. His research interests are in automated software testing and mining software engineering data. He has extensive experiences in applying and adapting data mining techniques on various types of industrial or open source software engineering data such as source code, execution traces, bug reports, and API doc. He co-presented tutorials on mining software engineering data at various conferences including ICSE, KDD, and ICDM. He was a co-organizer of 2007 Dagstuhl Seminar on Mining Programs and Processes. He was Program Co-Chair of 2009 IEEE International Conference on Software Maintenance (ICSM). [Homepage: http://people.engr.ncsu.edu/txie/]