Clustering-based association rule mining for bug assignee prediction

Abstract

Bug assignment is a decisive part of software maintenance. In this paper, we have proposed two approaches to apply association rule mining to assist bug triaging process. In the first approach, we have used apriori algorithm to predict the assignee of a newly reported bug based on the bug's severity, priority and summary terms. In the second approach, we have used X-means clustering followed by association rule mining inside each cluster. The redundant or identical meaning rules have been eliminated. We have analysed the association rules for top five assignees of Thunderbird, Add-on SDK and Bugzilla products of Mozilla open source project. We have also observed that the assignees who fixed Blocker and Critical bugs have less number of redundant rules in comparison of Normal bug fixers. Association rule mining after clustering results in rules with same or higher confidence.