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.
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Turhan, B., Bener, A., Menzies, T. (2010). Regularities in Learning Defect Predictors. In: Ali Babar, M., Vierimaa, M., Oivo, M. (eds) Product-Focused Software Process Improvement. PROFES 2010. Lecture Notes in Computer Science, vol 6156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13792-1_11
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DOI: https://doi.org/10.1007/978-3-642-13792-1_11
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