The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
프로젝트 간 결함 예측(CPDP)은 최근 몇 년간 뜨거운 연구 주제입니다. 소스 프로젝트와 대상 프로젝트 간의 일관되지 않은 데이터 분포와 대부분의 대상 인스턴스에 대한 레이블 부족으로 인해 결함 예측이 어려워집니다. 연구자들은 여러 가지 CPDP 방법을 개발했습니다. 그러나 예측 성능은 여전히 개선되어야 합니다. 본 논문에서는 JDAPL(Joint Domain Adaption and Pseudo-Labeling)이라는 새로운 접근 방식을 제안합니다. 네트워크 아키텍처는 소스 및 대상 인스턴스를 공통 하위 공간에 매핑하는 기능 매핑 하위 네트워크와 분류 하위 네트워크 및 보조 분류 하위 네트워크로 구성됩니다. 분류 하위 네트워크는 레이블이 지정된 인스턴스의 레이블 정보를 사용하여 의사 레이블을 생성합니다. 보조 분류 하위 네트워크는 손실 최대화를 통해 분포 차이를 줄이고 레이블이 지정되지 않은 인스턴스에 대한 의사 레이블의 정확도를 향상시키는 방법을 학습합니다. 네트워크 훈련은 적대적 계획에 따라 진행됩니다. AEEEM 및 NASA 데이터 세트의 10개 프로젝트에 대해 광범위한 실험이 수행되었으며 결과는 우리의 접근 방식이 기준선에 비해 더 나은 성능을 달성한다는 것을 나타냅니다.
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
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Fei WU, Xinhao ZHENG, Ying SUN, Yang GAO, Xiao-Yuan JING, "Joint Domain Adaption and Pseudo-Labeling for Cross-Project Defect Prediction" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 2, pp. 432-435, February 2022, doi: 10.1587/transinf.2021EDL8061.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDL8061/_p
부
@ARTICLE{e105-d_2_432,
author={Fei WU, Xinhao ZHENG, Ying SUN, Yang GAO, Xiao-Yuan JING, },
journal={IEICE TRANSACTIONS on Information},
title={Joint Domain Adaption and Pseudo-Labeling for Cross-Project Defect Prediction},
year={2022},
volume={E105-D},
number={2},
pages={432-435},
abstract={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.},
keywords={},
doi={10.1587/transinf.2021EDL8061},
ISSN={1745-1361},
month={February},}
부
TY - JOUR
TI - Joint Domain Adaption and Pseudo-Labeling for Cross-Project Defect Prediction
T2 - IEICE TRANSACTIONS on Information
SP - 432
EP - 435
AU - Fei WU
AU - Xinhao ZHENG
AU - Ying SUN
AU - Yang GAO
AU - Xiao-Yuan JING
PY - 2022
DO - 10.1587/transinf.2021EDL8061
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2022
AB - 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.
ER -