Revisiting "code smell severity classification using machine learning techniques".

In the context of limited maintenance resources, predicting the severity of code smells is more practically useful than simply detecting them. Fontana et al. first empirically investigated some classification algorithms and some regression algorithms, for severity prediction. Their results showed that random forest and decision tree performed well on Mean Absolute Error (MAE), Mean Squared Error (MSE), and Spearman and Kendall rank correlation coefficients. However, they did not consider the issue of imbalanced data distribution in the severity dataset, and used inappropriate performance evaluation metrics. Therefore, we revisit the effectiveness of 10 classification methods and 11 regression methods, for code severity prediction using Cumulative Lift Chart (CLC) and Severity@20% as the primary performance metrics and Accuracy as the secondary performance indicator. The results show that the Gradient Boosting Regression (GBR) method performs the best in terms of these metrics.