Cost-Sensitive and Sparse Ladder Network for Software Defect Prediction

Jing SUN; Yi-mu JI; Shangdong LIU; Fei WU

doi:10.1587/transinf.2019EDL8198

Cost-Sensitive and Sparse Ladder Network for Software Defect Prediction

Jing SUN, Yi-mu JI, Shangdong LIU, Fei WU

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Software defect prediction (SDP) plays a vital role in allocating testing resources reasonably and ensuring software quality. When there are not enough labeled historical modules, considerable semi-supervised SDP methods have been proposed, and these methods utilize limited labeled modules and abundant unlabeled modules simultaneously. Nevertheless, most of them make use of traditional features rather than the powerful deep feature representations. Besides, the cost of the misclassification of the defective modules is higher than that of defect-free ones, and the number of the defective modules for training is small. Taking the above issues into account, we propose a cost-sensitive and sparse ladder network (CSLN) for SDP. We firstly introduce the semi-supervised ladder network to extract the deep feature representations. Besides, we introduce the cost-sensitive learning to set different misclassification costs for defective-prone and defect-free-prone instances to alleviate the class imbalance problem. A sparse constraint is added on the hidden nodes in ladder network when the number of hidden nodes is large, which enables the model to find robust structures of the data. Extensive experiments on the AEEEM dataset show that the CSLN outperforms several state-of-the-art semi-supervised SDP methods.

Publication: IEICE TRANSACTIONS on Information Vol.E103-D No.5 pp.1177-1180

Publication Date: 2020/05/01

Publicized: 2020/01/29

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2019EDL8198

Type of Manuscript: LETTER

Category: Software Engineering

Authors

Jing SUN
  Nanjing University of Posts and Telecommunications (NJUPT)
Yi-mu JI
  Nanjing University of Posts and Telecommunications (NJUPT)
Shangdong LIU
  Nanjing University of Posts and Telecommunications (NJUPT)
Fei WU
  NJUPT

Keyword

semi-supervised learning, software defect prediction, ladder network, cost-sensitive learning, sparse auto-encoder

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Jing SUN, Yi-mu JI, Shangdong LIU, Fei WU, "Cost-Sensitive and Sparse Ladder Network for Software Defect Prediction" in IEICE TRANSACTIONS on Information, vol. E103-D, no. 5, pp. 1177-1180, May 2020, doi: 10.1587/transinf.2019EDL8198.
Abstract: Software defect prediction (SDP) plays a vital role in allocating testing resources reasonably and ensuring software quality. When there are not enough labeled historical modules, considerable semi-supervised SDP methods have been proposed, and these methods utilize limited labeled modules and abundant unlabeled modules simultaneously. Nevertheless, most of them make use of traditional features rather than the powerful deep feature representations. Besides, the cost of the misclassification of the defective modules is higher than that of defect-free ones, and the number of the defective modules for training is small. Taking the above issues into account, we propose a cost-sensitive and sparse ladder network (CSLN) for SDP. We firstly introduce the semi-supervised ladder network to extract the deep feature representations. Besides, we introduce the cost-sensitive learning to set different misclassification costs for defective-prone and defect-free-prone instances to alleviate the class imbalance problem. A sparse constraint is added on the hidden nodes in ladder network when the number of hidden nodes is large, which enables the model to find robust structures of the data. Extensive experiments on the AEEEM dataset show that the CSLN outperforms several state-of-the-art semi-supervised SDP methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8198/_p

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@ARTICLE{e103-d_5_1177,
author={Jing SUN, Yi-mu JI, Shangdong LIU, Fei WU, },
journal={IEICE TRANSACTIONS on Information},
title={Cost-Sensitive and Sparse Ladder Network for Software Defect Prediction},
year={2020},
volume={E103-D},
number={5},
pages={1177-1180},
abstract={Software defect prediction (SDP) plays a vital role in allocating testing resources reasonably and ensuring software quality. When there are not enough labeled historical modules, considerable semi-supervised SDP methods have been proposed, and these methods utilize limited labeled modules and abundant unlabeled modules simultaneously. Nevertheless, most of them make use of traditional features rather than the powerful deep feature representations. Besides, the cost of the misclassification of the defective modules is higher than that of defect-free ones, and the number of the defective modules for training is small. Taking the above issues into account, we propose a cost-sensitive and sparse ladder network (CSLN) for SDP. We firstly introduce the semi-supervised ladder network to extract the deep feature representations. Besides, we introduce the cost-sensitive learning to set different misclassification costs for defective-prone and defect-free-prone instances to alleviate the class imbalance problem. A sparse constraint is added on the hidden nodes in ladder network when the number of hidden nodes is large, which enables the model to find robust structures of the data. Extensive experiments on the AEEEM dataset show that the CSLN outperforms several state-of-the-art semi-supervised SDP methods.},
keywords={},
doi={10.1587/transinf.2019EDL8198},
ISSN={1745-1361},
month={May},}

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TY - JOUR
TI - Cost-Sensitive and Sparse Ladder Network for Software Defect Prediction
T2 - IEICE TRANSACTIONS on Information
SP - 1177
EP - 1180
AU - Jing SUN
AU - Yi-mu JI
AU - Shangdong LIU
AU - Fei WU
PY - 2020
DO - 10.1587/transinf.2019EDL8198
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2020
AB - Software defect prediction (SDP) plays a vital role in allocating testing resources reasonably and ensuring software quality. When there are not enough labeled historical modules, considerable semi-supervised SDP methods have been proposed, and these methods utilize limited labeled modules and abundant unlabeled modules simultaneously. Nevertheless, most of them make use of traditional features rather than the powerful deep feature representations. Besides, the cost of the misclassification of the defective modules is higher than that of defect-free ones, and the number of the defective modules for training is small. Taking the above issues into account, we propose a cost-sensitive and sparse ladder network (CSLN) for SDP. We firstly introduce the semi-supervised ladder network to extract the deep feature representations. Besides, we introduce the cost-sensitive learning to set different misclassification costs for defective-prone and defect-free-prone instances to alleviate the class imbalance problem. A sparse constraint is added on the hidden nodes in ladder network when the number of hidden nodes is large, which enables the model to find robust structures of the data. Extensive experiments on the AEEEM dataset show that the CSLN outperforms several state-of-the-art semi-supervised SDP methods.
ER -