Reduction of Redundant Rules in Association Rule Mining-Based Bug Assignment

TL;DR

This paper proposes a method for bug triaging that uses association rule mining on clustered bug data to predict bug assignees, demonstrating improved accuracy over existing techniques on Mozilla datasets.

Contribution

It introduces a clustering-based approach combined with association rule mining to reduce redundant rules and enhance bug assignment accuracy.

Findings

Improved bug assignment accuracy over existing methods

Effective use of clustering to handle large datasets

Validated on Mozilla bug reports with positive results

Abstract

Bug triaging is a process to decide what to do with newly coming bug reports. In this paper, we have mined association rules for the prediction of bug assignee of a newly reported bug using different bug attributes, namely, severity, priority, component and operating system. To deal with the problem of large data sets, we have taken subsets of data set by dividing the large data set using K-means clustering algorithm. We have used an Apriori algorithm in MATLAB to generate association rules. We have extracted the association rules for top 5 assignees in each cluster.The proposed method has been empirically validated on 14696 bug reports of Mozilla open source software project, namely, Seamonkey, Firefox and Bugzilla. The proposed method provides an improvement over the existing techniques for bug assignment problem.