¹Department of Computer Science, North Dakota State University, Fargo, ND 58108, USA
²Department of Mathematics, Computer Science and Software Engineering, University of Detroit Mercy

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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.

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