Volume 8 Number 9 ( Sep. 2013)
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JSW 2013 Vol.8(9): 2185-2190 ISSN: 1796-217X
doi: 10.4304/jsw.8.9.2185-2190

Efficient Bug Triaging Using Text Mining

Mamdouh Alenezi1, Kenneth Magel1, Shadi Banitaan2
1Department of Computer Science, North Dakota State University, Fargo, ND 58108, USA
2Department of Mathematics, Computer Science and Software Engineering, University of Detroit Mercy

Abstract—Large open source software projects receive abundant rates of submitted bug reports. Triaging these incoming reports manually is error-prone and time consuming. The goal of bug triaging is to assign potentially experienced developers to new-coming bug reports. To reduce time and cost of bug triaging, we present an automatic approach to predict a developer with relevant experience to solve the new coming report. In this paper, we investigate the use of five term selection methods on the accuracy of bug assignment. In addition, we re-balance the load between developers based on their experience. We conduct experiments on four real datasets. The experimental results show that by selecting a small number of discriminating terms, the F-score can be significantly improved.

Index Terms—Bug triage, term selection method, text classification, mining bug repositories.


Cite: Mamdouh Alenezi, Kenneth Magel, Shadi Banitaan, "Efficient Bug Triaging Using Text Mining," Journal of Software vol. 8, no. 9, pp. 2185-2190, 2013.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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