TRAM: An approach for assigning bug reports using their Metadata

Author

Banitaan, Shadi ; Alenezi, M.

Author_Institution

Dept. of Math., Comput. Sci. & Software Eng., Univ. of Detroit Mercy, Detroit, MI, USA

fYear

2013

fDate

19-21 June 2013

Firstpage

215

Lastpage

219

Abstract

Bug triage is an essential phase in the bug fixing process. The aim of bug triage is to assign an experienced developer to a new coming bug report. Existing bug triage approaches are mainly based on machine learning techniques. These approaches suffer from low prediction accuracy. In this paper, we propose TRAM (TRiaging Approach using bug reports Metadata). The goal is to improve the prediction accuracy of bug triage by utilizing the most discriminating terms of bug reports, the components in which the bugs belong to, and the reporter who filed the bug.We perform experimental evaluation on open-source projects namely Freedesktop, NetBeans, Eclipse, and Firefox. The results show that TRAM outperforms existing machine learning-based approaches in terms of classification accuracy. TRAM improves the F-score by approximately 34%, 40%, 20%, and 21% for Freedesktop, NetBeans, Eclipse, and Firfox respectively.

Keywords

learning (artificial intelligence); meta data; program debugging; Eclipse; Firefox; Firfox; Freedesktop; NetBeans; TRAM; bug fixing process; bug triage; machine learning; open-source projects; triaging approach using bug reports metadata; Accuracy; Computer bugs; Predictive models; Software; Software engineering; Training; Vectors; bug triage; classification; mining bug repositories; term selection method;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications and Information Technology (ICCIT), 2013 Third International Conference on

Conference_Location

Beirut

Print_ISBN

978-1-4673-5306-9

Type

conf

DOI

10.1109/ICCITechnology.2013.6579552

Filename

6579552