SMOTE and Feature Selection for More Effective Bug Severity Prediction

摘要

"Severity" is one of the essential features of software bug reports, which is a crucial factor for developers to decide which bug should be fixed immediately and which bug could be delayed to a next release. Severity assignment is a manual process and its accuracy depends on the experience of the assignee. Prior research proposed several models to automate this process. These models are based on textual preprocessing of historical bug reports and classification techniques. Although bug repositories suffer from severity class imbalance, none of the prior studies investigated the impact of implementing a class rebalancing technique on the accuracy of their models. In this paper, we propose a framework for predicting fine-grained severity levels which utilizes an over-sampling technique "SMOTE", to balance the severity classes, and a feature selection scheme, to reduce the data scale and select the most informative features for training a K-nearest neighbor (KNN) classifier. The KNN classifier utilizes a distance-weighted voting scheme to predict the proper severity level of a newly reported bug. We investigated the effectiveness of our proposed approach on two large bug repositories, namely Eclipse and Mozilla, and the experimental results showed that our approach outperforms cutting-edge studies in predicting the minority severity classes.