Title: Clustering-based association rule mining for bug assignee prediction

Authors: Meera Sharma; V.B. Singh

Addresses: Department of Computer Science, University of Delhi, Delhi, India ' Delhi College of Arts and Commerce, University of Delhi, Delhi, India

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.

Keywords: bug triaging; bug severity; bug priority; bug summary; association rules mining; X-means clustering; bug assignee prediction; bug assignment; software maintenance; software bugs; bug fixing; redundant rules; software development.

DOI: 10.1504/IJBIDM.2016.081606

International Journal of Business Intelligence and Data Mining, 2016 Vol.11 No.2, pp.130 - 150

Received: 16 Feb 2016
Accepted: 05 Jul 2016
Published online: 17 Jan 2017 *

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