Predicting the severity of a reported bug
Predicting the severity of a reported bug
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DOI：
10.1109/msr.2010.5463284
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发表时间：
2010-05
期刊：
影响因子：
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通讯作者：
Ahmed Lamkanfi;S. Demeyer;E. Giger;Bart Goethals
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文献类型：
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作者：
Ahmed Lamkanfi;S. Demeyer;E. Giger;Bart Goethals
不幸的是，报告的错误的严重性是确定需要确定多久的关键因素，而对于如何分配错误的严重性，它仍然是继承的手动过程。在本文中，我们研究了使用文本挖掘算法分析其文本描述，是否可以准确预测报告的错误的严重性。 Eclipse和Gnome），我们包括给定足够尺寸的训练集（大约500个严重程度的报告），可以以合理的精度预测严重性（精度和召回率在0.65-0.75之间，Mozilla和Eclipse在0.65-0.75之间变化； 0.70 –0.85在侏儒的情况下）。
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).