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Predicting the severity of a reported bug


Lamkanfi, A; Demeyer, S; Giger, E; Goethals, B (2010). Predicting the severity of a reported bug. In: 7th Working Conference on Mining Software Repositories, Cape Town, South Africa, 2 May 2010 - 3 May 2010, 1-10.

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).

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).

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:3 May 2010
Deposited On:28 Feb 2011 08:13
Last Modified:12 Aug 2017 03:04
Series Name:MSR'10
Free access at:Related URL. An embargo period may apply.
Related URLs:http://www.ifi.uzh.ch/pax/index.php/publication/show/id/1573 (Author)

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