Bug Severity Prediction Using a Hierarchical One-vs.-Remainder Approach

Abstract

Assigning severity level to reported bugs is a critical part of software maintenance to ensure an efficient resolution process. In many bug trackers, e.g. Bugzilla, this is a time consuming process, because bug reporters must manually assign one of seven severity levels to each bug. In addition, some bug types may be reported more often than others, leading to a disproportionate distribution of severity labels. Machine learning techniques can be used to predict the label of a newly reported bug automatically. However, learning from imbalanced data in a multi-class task remains one of the major difficulties for machine learning classifiers. In this paper, we propose a hierarchical classification approach that exploits class imbalance in the training data, to reduce classification bias. Specifically, we designed a classification tree that consists of multiple binary classifiers organised hierarchically, such that instances from the most dominant class are trained against the remaining classes but are not used for training the next level of the classification tree. We used FastText classifier to test and compare between the hierarchical and standard classification approaches. Based on 93,051 bug reports from 38 Eclipse open-source products, the hierarchical approach was shown to perform relatively well with 65 % Micro F-Score and 45 % Macro F-Score.