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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Predicting the severity of a reported bug |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Page Range | 1 - 10 |
| Event Title | 7th Working Conference on Mining Software Repositories |
| Event Type | conference |
| Event Location | Cape Town, South Africa |
| Event Start Date | May 2 - 2010 |
| Event End Date | May 3 - 2010 |
| Series Name | MSR'10 |
| Abstract Text | 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). |
| Free access at | Related URL |
| Digital Object Identifier | 10.1109/MSR.2010.5463284 |
| Other Identification Number | merlin-id:69 |
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