Automated Priority Prediction for Bug Reports Using Comment Intensiveness Features and SMOTE Data Balancing

Abstract

The processing priorities for software bug reports are important for software maintenance. Predicting the priorities for bug reports is the subject of many software engineering studies. This study proposes a priority prediction method that uses comment intensiveness features and a Synthetic Minority Over-sampling Technique (SMOTE)-based data balancing scheme. Experiments use datasets for three open-source projects: Eclipse, Mozilla and OpenOffice. The effectiveness of the proposed approach is determined using five classification models: Multinomial Naïve Bayes, Support Vector Machines, Random Forest, Extra Trees and eXtreme Gradient Boosting. The results show that the CIS-SMOTE-based models achieve 0.6078 Precision, 0.4927 Recall, 0.4465 F1-score and 0.7836 Accuracy in priority perdition. The results also show that CIS-SMOTE-RF, CIS-SMOTE-ET and CIS-SMOTE-XGB outperform two advanced priority prediction approaches, eApp and cPur, in terms of all performance measures.