Abstract-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).
Lamkanfi et al. REF extract textual features from the bug reports to predict the severity of the bug reports to assist in triaging.
Predicting the severity of a reported bug
2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)
2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)
Computer Science