Firefly Optimization-Based Feature Selection for Software Defect Density Prediction

Abstract

Software defect density prediction is essential for improving software quality and reliability; however, ac- curate prediction is challenging due to feature redundancy and imbalanced regression data. To address these issues, this study proposes a hybrid framework integrating Recursive Feature Elimination with Cross- Validation (RFECV), GridSearchCV, and Firefly Optimization (FFO) for feature selection and hyperpa- rameter optimization, along with SMOGN for imbalance handling. The framework is evaluated on six datasets using Gradient Boosting, Random Forest, Bagging, AdaBoost, Voting, and XGBoost regression models. Performance is assessed using MSE, MAE, RMSE, and SERA metrics. Experimental results show significant improvement, with RMSE reduced from 7.96 to 0.10 on the healthcare dataset and from 2.06 to 0.056 on the ANT 1.5 dataset. Comparisons with PSO, GWO, and GA further demonstrate superior accuracy, stability, and convergence behavior of FFO. Statistical validation using the Wilcoxon signed- rank test confirms the effectiveness and robustness of the proposed framework for software defect density prediction.