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).
By analyzing the textual description from bug reports and using text mining algorithms, Lamkanfi et al. REF conducted case studies on three large-scale open source projects, and based on the result, concluded that the technique is able to predict the severity with a reasonable accuracy.
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