Improving bug triage with relevant search

Abstract

Bug triage is a process where bugs are assigned to developers. In large open source projects such as Mozilla and Eclipse, bug triage is time-consuming because numerous bugs are submitted everyday. To improve bug triage, many studies have proposed automatic approaches to recommend proper developers for resolving bugs. These approaches are based on machine learning algorithms, which treat bug triage like text classification. Although they are effective, the accuracy of them can be further improved. Our goal is to propose a method not only has good performance but also is simple. We propose a method based on relevant search technique to recommend developers for the given bugs. First, we construct an index for bugs to make them searchable. Then, for a given bug to be assigned, we utilize the index to search for the bugs related to it. Finally, we analyze these related bugs and recommend developers based on them. We conduct experiments on bugs of Mozilla and Eclipse to evaluate our method. The results indicate that our method has a good performance and outperforms machine learning algorithms like Naïve Bayes and SVM.