Abstract
Large open source bug tracking systems receives large number of bug reports daily. Managing these huge numbers of incoming bug reports is a challenging task. Dealing with these reports manually consumes time and resources which leads to delaying the resolution of important bugs which are crucial and need to be identified and resolved earlier. Bug triaging is an important process in software maintenance. Some bugs are important and need to be fixed right away, whereas others are minor and their fixes could be postponed until resources are available. Most automatic bug assignment approaches do not take the priority of bug reports in their consideration. Assigning bug reports based on their priority may play an important role in enhancing the bug triaging process. In this paper, we present an approach to predict the priority of a reported bug using different machine learning algorithms namely Naive Bayes, Decision Trees, and Random Forest. We also investigate the effect of using two feature sets on the classification accuracy. We conduct experimental evaluation using open-source projects namely Eclipse and Firefox. The experimental evaluation shows that the proposed approach is feasible in predicting the priority of bug reports. It also shows that feature-set-2 outperforms feature-set-1. Moreover, both Random Forests and Decision Trees outperform Naive Bayes.