%0 Conference Paper %B 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010) %D 2010 %T Predicting the severity of a reported bug %A Lamkanfi, Ahmed %A Demeyer, Serge %A Giger, Emanuel %A Goethals, Bart %K bug reports %K eclipse %K gnome %K mozilla %K severity %K text mining %X The severity of a reported bug is a critical factor in deciding how soon it needs to be fixed. Unfortunately, while clear guidelines exist on how to assign the severity of a bug, it remains an inherent manual process left to the person reporting the bug. In this paper we investigate whether we can accurately predict the severity of a reported bug by analyzing its textual description using text mining algorithms. Based on three cases drawn from the open-source community (Mozilla, Eclipse and GNOME), we conclude that given a training set of sufficient size (approximately 500 reports per severity), it is possible to predict the severity with a reasonable accuracy (both precision and recall vary between 0.65-0.75 with Mozilla and Eclipse; 0.70-0.85 in the case of GNOME). %B 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010) %I IEEE %C Cape Town, South Africa %P 1 - 10 %@ 978-1-4244-6802-7 %R 10.1109/MSR.2010.5463284 %> https://flosshub.org/sites/flosshub.org/files/1lamkanfiDemeyer1.pdf