Assembling Relevant Bug Report using the Constraint-based k-means Clustering

Abstract

Bug reports provide an important information for improving software quality. Today, many bug tracking systems (BTS) such as Bugzilla, Jira, Mantis, and Trac are developed for collecting bug reports from users around the world. Unfortunately, many tasks on the BTS are still performed manually by bug triagers. The process is time consuming and errors prone. Although many studies on bug reports have been proposed, the problems may have never been truly investigated. It is the problem of bug dependency which is when an unfixed bug‘a’ affects bug ‘b’. As a result, bug‘b cannot be fixed if bug ‘a’ is not fixed. To address this problem, the relevant bug reports must be assigned to the same specific category in order to help the developers recognize all bugs that are indicating to the same problem domain. Bug dependency is a time-consuming and labor-intensive process. This is a challenge issue. Therefore, this work aims to present a method for assembling the relevant bug reports into specific clusters by the modifiedk-mean clustering algorithm, called the constraint-based k-means clustering. Furthermore, three weighting methods oftf, tf-idf, and BM25 are compared. After testing by recall, precision, andF-measure, the results reveal good score in precision but the recall score should be improved. The method withtf returns the better results than tf-idf and BM25 methods because tf method is based on the local weight that has paid towards a specific cluster-oriented.ster-oriented.