Optimization of Tree Algorithms by Resampling and Ensembling in Deffect Prediction Software
Article Sidebar
Main Article Content
Abstract
The level of defects in a software will always be linear with the quality of the resulting software. In the development process, developers need to predict the level of defects in a software to produce better software. In this study, the Particle Swarm optimization (PSO) method was used to optimize the data at the preprocessing stage, the Random Over Sampling (ROS) method to balance the classes in the dataset and the ensemble technique to maximize the performance of the J48 algorithm. The dataset used in this study uses the PROMISE repository dataset. The results showed that the integration of the PSO+ROS+J48+Bagging algorithm resulted in an average accuracy value of 92.378% and an AUC value of 0.924. This shows that the combination of PSO, ROS and J48 methods with Bagging Technique is feasible to be used as an algorithm to predict the defect level of a software
Downloads
Article Details
[2] Faruk, Ö. (2015). Software defect prediction using cost-sensitive neural network. Elsevier, 33, 263–277. https://doi.org/10.1016/j.asoc.2015.04.045
[3] Fitriani, & Wahono, R. S. (2015). Integrasi Bagging dan Greedy Forward Selection pada Prediksi Cacat Software dengan Menggunakan Naïve Bayes. Journal of Software Engineering, 1(2), 101–108.
[4] Khoshgoftaar, T. M. (2010). Attribute Selection and Imbalanced Data?: Problems in Software Defect Prediction. https://doi.org/10.1109/ICTAI.2010.27 Kim, J. J., Ja,
[5] Saifudin, A., & Wahono, R. S. (2015). Penerapan Teknik Ensemble untuk Menangani Ketidakseimbangan Kelas pada Prediksi Cacat Software. 1(1).
[6] Sathyaraj, R., & Prabu, S. (2015). An Approach for Software Fault Prediction to Measure the Quality of Diferent Prediction Methodologies using Software Metrics. 8(December). https://doi.org/10.17485/ijst/2015/v8i35/73717
[7] Wahono, R. S., Suryana, N., & Ahmad, S. (2014). Metaheuristic Optimization based Feature Selection for Software Defect Prediction. 9(5), 1324–1333. https://doi.org/10.4304/jsw.9.5.1324-1333
[8] Zheng, J. (2010). Expert Systems with Applications Cost-sensitive boosting neural networks for software defect prediction. Expert Systems With Applications, 37(6), 4537–4543. https://doi.org/10.1016/j.eswa.2009.12.056
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.