Reduction of Redundant Rules in Association Rule Mining-Based Bug Assignment
This work addresses bug triaging for software developers, but it is incremental as it builds on existing association rule mining methods.
The authors tackled bug assignment by mining association rules from bug attributes and using K-means clustering to handle large datasets, achieving an improvement over existing techniques on 14,696 bug reports from Mozilla projects.
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.