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Joint Domain Adaption and Pseudo-Labeling for Cross-Project Defect Prediction

Fei WU, Xinhao ZHENG, Ying SUN, Yang GAO, Xiao-Yuan JING

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Summary :

Cross-project defect prediction (CPDP) is a hot research topic in recent years. The inconsistent data distribution between source and target projects and lack of labels for most of target instances bring a challenge for defect prediction. Researchers have developed several CPDP methods. However, the prediction performance still needs to be improved. In this paper, we propose a novel approach called Joint Domain Adaption and Pseudo-Labeling (JDAPL). The network architecture consists of a feature mapping sub-network to map source and target instances into a common subspace, followed by a classification sub-network and an auxiliary classification sub-network. The classification sub-network makes use of the label information of labeled instances to generate pseudo-labels. The auxiliary classification sub-network learns to reduce the distribution difference and improve the accuracy of pseudo-labels for unlabeled instances through loss maximization. Network training is guided by the adversarial scheme. Extensive experiments are conducted on 10 projects of the AEEEM and NASA datasets, and the results indicate that our approach achieves better performance compared with the baselines.

Publication
IEICE TRANSACTIONS on Information Vol.E105-D No.2 pp.432-435
Publication Date
2022/02/01
Publicized
2021/11/04
Online ISSN
1745-1361
DOI
10.1587/transinf.2021EDL8061
Type of Manuscript
LETTER
Category
Software Engineering

Authors

Fei WU
  Nanjing University of Posts and Telecommunications
Xinhao ZHENG
  Nanjing University of Posts and Telecommunications
Ying SUN
  Nanjing University of Posts and Telecommunications
Yang GAO
  Nanjing University of Posts and Telecommunications
Xiao-Yuan JING
  Wuhan University

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