Improving the performance of TFS with ensemble learning for cross-project software defect prediction

Abstract—Software defect prediction (SDP) plays a key role in improving software quality by identifying defect-prone modules early in the development cycle. While within-project prediction has been widely studied, cross-project defect prediction (CPDP) remains challenging due to differences in datasets, high feature dimensionality, and poor model generalization. To address these challenges, this study enhances the Transformation and Feature Selection (TFS) approach by integrating ensemble learning techniques. Three methods, Gradient Boosting Machine (GBM), stacking, and hybridization, were explored to evaluate their effectiveness in improving CPDP performance. Experiments were conducted using the AEEEM datasets, with preprocessing steps including normalization, feature reduction, and the Synthetic Minority Oversampling Technique (SMOTE) to handle data imbalance. The models were trained on source projects and tested on separate target projects, with the F1 score used as the main evaluation metric. Results show that the TFS × Stacking model achieved the highest overall performance, with a mean F1 score of 0.963, outperforming both TFS × GBM (0.958) and TFS × Hybridization (0.920). Compared to the original TFS × Random Forest method, the stacking approach consistently provided significant improvements across all project pairs. These findings highlight the potential of combining TFS with ensemble learning to enhance defect prediction in projects with limited or no historical data. This work not only advances CPDP research but also offers practical value to software teams by enabling more accurate identification of defect-prone modules and better allocation of testing resources.

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