@inproceedings{073c3191167643b6ae9b180696aad65d,
title = "Improving prediction robustness of VAB-SVM for cross-project defect prediction",
abstract = "Software defect prediction is important for improving software quality. Defect predictors allow software test engineers to focus on defective modules. Cross-Project Defect Prediction (CPDP) uses data from other companies to build defect predictors. However, outliers may lower prediction accuracy. In this study, we propose a transfer learning based model called VAB-SVM for CPDP robust in handling outliers. Notably, this method deals with the class imbalance problem which may decrease the prediction accuracy. Our proposed method computes similarity weights of the training data based on the test data. Such weights are applied to Boosting algorithm considering the class imbalance. VAB-SVM outperformed the previous research more than 10\% and showed a sufficient robustness regardless of the ratio of outliers.",
keywords = "Boosting, Cross-project defect prediction, Outlier detection, Transfer learning",
author = "Duksan Ryu and Okjoo Choi and Jongmoon Baik",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 17th IEEE International Conference on Computational Science and Engineering, CSE 2014 - Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014 ; Conference date: 19-12-2014 Through 21-12-2014",
year = "2015",
month = jan,
day = "26",
doi = "10.1109/CSE.2014.198",
language = "English",
series = "Proceedings - 17th IEEE International Conference on Computational Science and Engineering, CSE 2014, Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "994--999",
editor = "Xingang Liu and \{El Baz\}, Didier and Ching-Hsien Hsu and Kai Kang and Weifeng Chen",
booktitle = "Proceedings - 17th IEEE International Conference on Computational Science and Engineering, CSE 2014, Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014",
}