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Integrating Adaptive Sampling with Ensembles Model for Software Defect Prediction
Corresponding Author(s) : Siti Rochimah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 10, No. 2, May 2025
Abstract
Handling class imbalance is a challenge in software defect prediction. Imbalanced datasets can cause bias in machine learning models, hindering their ability to detect defects. This paper proposes an integration of Adaptive Synthetic Sampling (ADASYN) and ensemble learning methods to improve prediction accuracy. ADASYN enhances the handling of imbalanced data by generating synthetic samples for hard-to-classify instances, while the ensemble stacking technique leverages the strengths of multiple models to reduce bias and variance. The machine learning model used in this study is K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF). The results demonstrate that ADASYN combined with ensemble stacking outperforms the traditional SMOTE technique in most cases. For instance, in the Ant-1.7 dataset, ADASYN achieved a stacking accuracy of 90.60% compared to 89.32% with SMOTE. Similarly, in the Camel-1.6 dataset, ADASYN achieved 91.56%, slightly exceeding SMOTE’s 91.32%. However, SMOTE performed better in simpler models like Decision Tree for certain datasets, highlighting the importance of choosing the appropriate resampling method. Across all datasets, ensemble stacking consistently provided the highest accuracy, benefiting from ADASYN's adaptive resampling strategy. These results underscore the importance of combining advanced sampling methods with ensemble learning techniques to address class imbalance effectively. This approach improves prediction accuracy and provides a practical framework for reliable software defect prediction in real-world scenarios. Future work will explore hybrid techniques and broader evaluations across diverse datasets and classifiers.
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A. Kurani, P. Doshi, A. Vakharia, and M. Shah, “A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting,” Ann. Data Sci., vol. 10, no. 1, pp. 183–208, Feb. 2023, doi: 10.1007/s40745-021-00344-x.
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K. Vos, Z. Peng, C. Jenkins, M. R. Shahriar, P. Borghesani, and W. Wang, “Vibration-based anomaly detection using LSTM/SVM approaches,” Mech. Syst. Signal Process., vol. 169, p. 108752, Apr. 2022, doi: 10.1016/j.ymssp.2021.108752.
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X. Pu et al., “Improving Lower Limb Function and Frailty in Frail Older Patients with Acute Myocardial Infarction After Percutaneous Coronary Intervention: A Randomized Controlled Study of Neuromuscular Electrical Stimulation,” Clin. Interv. Aging, vol. Volume 19, pp. 1163–1176, Jul. 2024, doi: 10.2147/CIA.S460805.
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M. K. Suryadi, R. Herteno, S. W. Saputro, M. R. Faisal, and R. A. Nugroho, “Comparative Study of Various Hyperparameter Tuning on Random Forest Classification With SMOTE and Feature Selection Using Genetic Algorithm in Software Defect Prediction,” J. Electron. Electromed. Eng. Med. Inform., vol. 6, no. 2, pp. 137–147, Mar. 2024, doi: 10.35882/jeeemi.v6i2.375.
A. W. Dar and S. U. Farooq, “Handling class overlap and imbalance using overlap driven under-sampling with balanced random forest in software defect prediction,” Innov. Syst. Softw. Eng., Jun. 2024, doi: 10.1007/s11334-024-00571-4.
S. Goyal, “Heterogeneous Stacked Ensemble Classifier for Software Defect Prediction,” in 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, India: IEEE, Nov. 2020, pp. 126–130. doi: 10.1109/PDGC50313.2020.9315754.
S. Feng, J. Keung, X. Yu, Y. Xiao, and M. Zhang, “Investigation on the stability of SMOTE-based oversampling techniques in software defect prediction,” Inf. Softw. Technol., vol. 139, p. 106662, Nov. 2021, doi: 10.1016/j.infsof.2021.106662.
A. Kurani, P. Doshi, A. Vakharia, and M. Shah, “A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting,” Ann. Data Sci., vol. 10, no. 1, pp. 183–208, Feb. 2023, doi: 10.1007/s40745-021-00344-x.
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Y. Z. Bala, P. Abdul Samat, K. Y. Sharif, and N. Manshor, “Improving Cross-Project Software Defect Prediction Method Through Transformation and Feature Selection Approach,” IEEE Access, vol. 11, pp. 2318–2326, 2023, doi: 10.1109/ACCESS.2022.3231456.
M. H. D. M. Ribeiro, R. G. Da Silva, S. R. Moreno, V. C. Mariani, and L. D. S. Coelho, “Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting,” Int. J. Electr. Power Energy Syst., vol. 136, p. 107712, Mar. 2022, doi: 10.1016/j.ijepes.2021.107712.
T. Wu, W. Zhang, X. Jiao, W. Guo, and Y. Alhaj Hamoud, “Evaluation of stacking and blending ensemble learning methods for estimating daily reference evapotranspiration,” Comput. Electron. Agric., vol. 184, p. 106039, May 2021, doi: 10.1016/j.compag.2021.106039.
Z. Ding and L. Xing, “Improved software defect prediction using Pruned Histogram-based isolation forest,” Reliab. Eng. Syst. Saf., vol. 204, p. 107170, Dec. 2020, doi: 10.1016/j.ress.2020.107170.
W. Huang et al., “Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM,” Appl. Soft Comput., vol. 109, p. 107541, Sep. 2021, doi: 10.1016/j.asoc.2021.107541.
M. Rashid, J. Kamruzzaman, T. Imam, S. Wibowo, and S. Gordon, “A tree-based stacking ensemble technique with feature selection for network intrusion detection,” Appl. Intell., vol. 52, no. 9, pp. 9768–9781, Jul. 2022, doi: 10.1007/s10489-021-02968-1.
N. A. A. Khleel and K. Nehéz, “Software defect prediction using a bidirectional LSTM network combined with oversampling techniques,” Clust. Comput., vol. 27, no. 3, pp. 3615–3638, Jun. 2024, doi: 10.1007/s10586-023-04170-z.
N. Kardani, A. Zhou, M. Nazem, and S.-L. Shen, “Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data,” J. Rock Mech. Geotech. Eng., vol. 13, no. 1, pp. 188–201, Feb. 2021, doi: 10.1016/j.jrmge.2020.05.011.
A. Chatzimparmpas, R. M. Martins, K. Kucher, and A. Kerren, “StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics,” IEEE Trans. Vis. Comput. Graph., vol. 27, no. 2, pp. 1547–1557, Feb. 2021, doi: 10.1109/TVCG.2020.3030352.
R. Lazzarini, H. Tianfield, and V. Charissis, “A stacking ensemble of deep learning models for IoT intrusion detection,” Knowl.-Based Syst., vol. 279, p. 110941, Nov. 2023, doi: 10.1016/j.knosys.2023.110941.