Predicting the severity of a reported bug
作者： Ahmed Lamkanfi , Serge Demeyer , Emanuel Giger , Bart Goethals
关键词: Statistics 、 Training set 、 Precision and recall 、 Gnome 、 Software debugging 、 Data mining 、 Software bug 、 Computer science 、 Open source software 、 Public domain software 、 Eclipse
摘要: The severity of a reported bug is critical factor in deciding how soon it needs to be fixed. Unfortunately, while clear guidelines exist on assign the bug, remains an inherent manual process left person reporting bug. In this paper we investigate whether can accurately predict by analyzing its textual description using text mining algorithms. Based three cases drawn from open-source community (Mozilla, Eclipse and GNOME), conclude that given training set sufficient size (approximately 500 reports per severity), possible with reasonable accuracy (both precision recall vary between 0.65–0.75 Mozilla Eclipse; 0.70–0.85 case GNOME).
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