An Exploratory Study of Bug Prioritization and Severity Prediction based on Source Code Features

Abstract

Software systems generate a large number of bugs during their lifecycles. Managing and assigning these bug reports is a challenging task. Building prediction models for the priority or severity levels of bugs through bug reports can help developers prioritize highly urgent bugs. Traditional prediction models are based on the textual description information in bug reports. However, most of the description is little or no. According to the bug report, developers need to fix the corresponding source code files. If the corresponding source code file is a core module in a software system, the report is likely to have high-level assignment rights. Therefore, in this paper, we investigate the effect of using the source code file feature sets on classification performance. In addition, we evaluate the effect of different sampling methods on the data, namely SMOTE, RUS, SMOTEEN, Adaboost, and GAN. Extensive experiments were conducted on five open-source projects. The experimental results show that the source code file feature sets do not perform as well as the textual description features in bug reports. Besides, over-sampling methods do not alleviate the data imbalance problem in the case of insufficient data, while GAN performs best in the case of sufficient data.