Self-Adaptive Parameter Control in Differential Evolution Based on Optimizing Undersampling Strategies for Software Defect Prediction

Data imbalance is a fundamental challenge in software defect prediction (SDP), as the dominance of non-defect classes biases models and reduces the ability to detect defective modules. Although Learning-to-Rank Undersampling (LTRUS) has been shown to be more effective than random undersampling; however, its performance still depends on the determination of static ranking weights (A), which are prone to stagnation during optimization. This study proposes an adaptive framework that integrates LTRUS with four variants of differential evolution, namely JADE, SHADE, L SHADE, and SaWDE, to dynamically optimize the weight (A). Experiments were conducted using a 5-fold cross-validation scheme and three classifiers: K-Nearest Neighbors, Logistic Regression, and Naïve Bayes. Performance was evaluated using AUC, Matthews Correlation Coefficient, Precision, Recall, and F Measure, along with statistical analysis based on the Shapiro-Wilk normality test and paired t-test. The results showed that JADE performed best on KNN, with AUC 0.725, MCC 0.356, and F Measure 0.450, and on Logistic Regression, with AUC 0.750, MCC 0.397, and F Measure 0.474. In Naïve Bayes, differential evolution was most effective with an AUC of 0.784, MCC of 0.441, and F Measure of 0.515. The results indicate that performance improvements are statistically significant in several cases, particularly for KNN and Logistic Regression, although not all comparisons show significant differences.