Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55591
Title: Predicting severity of bug report by mining bug repository with concept profile
Authors: Zhang, T
Yang, G
Lee, B
Chan, ATS 
Keywords: Bug report
Bug triage
Concept profile
Mining bug repository
Severity prediction
Software maintenance
Issue Date: 2015
Publisher: Association for Computing Machinery
Source: Proceedings of the 30th Annual ACM Symposium on Applied Computing, 13-17 April 2015, p. 1553-1558 How to cite?
Abstract: Recently, for large scale software projects, developers rely on bug reports for corrective software maintenance. The severity of a reported bug is an important feature to decide how fast it needs to be fixed. Therefore, to arrange a new submitted bug to an appropriate fixer, it is necessary to recognize the severity of each bug report. Unfortunately, reporters need to decide the severity of bugs manually. Even if there are guidelines on how to verify the severity of a bug, it is still a time-consuming work. Utilizing the concept profiles by mining bug repositories is a good way to resolve this problem. In this paper, we propose a concept profile-based prediction technique to assign the severity of a given bug. In detail, we analyze historical bug reports in the bug repositories and build the concept profiles from them. We evaluate the performance of our method on the bug reports from the bug repositories of popular open-source projects that include Eclipse and Mozilla Firefox, the result shows that the proposed technique can effectively predict the severity of a given bug.
URI: http://hdl.handle.net/10397/55591
ISBN: 9781450331968
DOI: 10.1145/2695664.2695872
Appears in Collections:Conference Paper

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