This is a special remote video talk, held in Schreiber 309

Speaker: David Lo, Singapore Management University

Title: Software Analytics: Approaches, Challenges and Opportunities


Abstract:
There is a huge mass of software data available to be mined including
execution traces, bug reports, software forums, etc. For many of these
datasets, traditional static analysis techniques could not be used.
Thus there is a need for new specialized techniques and the
applications of techniques borrowed from areas like data mining,
natural language processing, and information retrieval to help the
task of analyzing these diverse varieties of software data.

This talk focuses on mining from traces collected during the execution
of a program, bug reports expressed in natural language, and other
non-code software data. It highlights various challenges due to the
diversity of the data, the quality of the data, the suitability of
various off-the-shelf mining algorithms, and the complexity of various
mining techniques when applied to software engineering datasets. The
talk then proceeds to describe some potential generic techniques that
could be used to address the issues. Opportunities in terms of open
technical problems and potential benefits on mining various kinds of
software data will also be highlighted.

Biodata:
David Lo is an assistant professor in the School of Information
Systems, Singapore Management University. His research interests
include dynamic program analysis, specification mining, debugging,
social network analysis, and pattern mining. He has worked on the
extraction of behavioral models from execution logs, the analysis of
textual bug reports, and other studies that analyze wide varieties of
software data sources. For these problems, he has investigated the use
of data mining, information retrieval, and natural language processing
techniques. Lo received a PhD in computer science from the National
University of Singapore in 2008.
http://www.mysmu.edu/faculty/davidlo/