Regularities in learning defect predictors

Authors:
Burak Turhan;Ayse Bener;Tim Menzies
Affiliations:
Department of Information Processing Science, University of Oulu, Oulu, Finland;Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey;Lane Dept. of CS&EE, West Virginia University, Morgantown, WV
Venue:
PROFES'10 Proceedings of the 11th international conference on Product-Focused Software Process Improvement
Year:
2010

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Abstract

Collecting large consistent data sets of real world software projects from a single source is problematic. In this study, we show that bug reports need not necessarily come from the local projects in order to learn defect prediction models. We demonstrate that using imported data from different sites can make it suitable for predicting defects at the local site. In addition to our previous work in commercial software, we now explore open source domain with two versions of an open source anti-virus software (Clam AV) and a subset of bugs in two versions of GNU gcc compiler, to mark the regularities in learning predictors for a different domain. Our conclusion is that there are surprisingly uniform assets of software that can be discovered with simple and repeated patterns in local or imported data using just a handful of examples.