Boosting Software Fault Prediction Accuracy with Ensemble Learning

Software defects are natural quality characteristics of software that are difficult to eliminate completely, even with concerted efforts. In addition to Bayes Net, in this study, C4.5 Decision Tree, Multilayer Perceptron (MLP), and Random Forests (RFs) are used. Moreover, an ensemble strategy with GNB, BNB, RF, and MLP is proposed to enhance the prediction accuracy. Results from empirical evaluations indicate that the F1 score, accuracy, precision, and recall of this strategy are higher than those of any individual approach, providing strong evidence for the ensemble model as an effective method for improving defect prediction performance. The ensemble approach could be a promising pathway to bolster the software quality process, mainly in machine learning-based fault prediction.