Efficient Bug Triaging Using Text Mining

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 by clustering similar bug reports and ranking developers in each cluster 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.