TY - GEN
T1 - Software defect prediction using transfer method
AU - Ma, Ying
AU - Luo, Guangchun
AU - Li, Jiong
AU - Chen, Aiguo
PY - 2011
Y1 - 2011
N2 - Traditional machine learning works well within company defect prediction. Unlike these works, we consider the scenario where source and target data are drawn from different companies, recently referred to as cross-company defect prediction. In this paper, we proposed a novel algorithm based on transfer method, called Transfer Naive Bayes (TNB). Our solution transferred the information of test data to the weights of the training data. The theoretical analysis and experiment results indicate that our algorithm is able to get more accurate result within less runtime cost than the state of the art algorithm.
AB - Traditional machine learning works well within company defect prediction. Unlike these works, we consider the scenario where source and target data are drawn from different companies, recently referred to as cross-company defect prediction. In this paper, we proposed a novel algorithm based on transfer method, called Transfer Naive Bayes (TNB). Our solution transferred the information of test data to the weights of the training data. The theoretical analysis and experiment results indicate that our algorithm is able to get more accurate result within less runtime cost than the state of the art algorithm.
UR - https://www.scopus.com/pages/publications/84055200196
U2 - 10.1109/ICCPS.2011.6092261
DO - 10.1109/ICCPS.2011.6092261
M3 - 会议稿件
AN - SCOPUS:84055200196
SN - 9781457706035
T3 - 2011 International Conference on Computational Problem-Solving, ICCP 2011
SP - 610
EP - 613
BT - 2011 International Conference on Computational Problem-Solving, ICCP 2011
T2 - 2011 International Conference on Computational Problem-Solving, ICCP 2011
Y2 - 21 October 2011 through 23 October 2011
ER -