Ying Sun1, Yanfei Sun1,2,*, Jin Qi1, Fei Wu1, Xiao-Yuan Jing1,3, Yu Xue4, Zixin Shen5
CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3373-3389, 2021, DOI:10.32604/cmc.2021.016539
Abstract Cross-project defect prediction (CPDP) aims to predict the defects on target project by using a prediction model built on source projects. The main problem in CPDP is the huge distribution gap between the source project and the target project, which prevents the prediction model from performing well. Most existing methods overlook the class discrimination of the learned features. Seeking an effective transferable model from the source project to the target project for CPDP is challenging. In this paper, we propose an unsupervised domain adaptation based on the discriminative subspace learning (DSL) approach for CPDP. DSL treats the data from two… More >
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