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

arXiv: https://arxiv.org/abs/1807.08906 · HTML: https://mlsanity.com/paper/1807.08906/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Comparison of Machine Learning Methods for Assigning Software Issues to Team Members](https://mlsanity.com/paper/2307.00009/) (2023)

## Abstract (arXiv)

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.