基于二次学习的半监督字典学习软件缺陷预测<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201703006

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Abstract When the previous defect labels of modules in software history warehouse are limited, building an effective prediction model becomes a challenging problem. Aiming at this problem, a twice learning based semi-supervised learning algorithm for software defect prediction is proposed. In the first stage of learning, a large number of unlabeled samples are labeled with probability soft labels and extended to the labeled training dataset by using sparse representation classifier. Then, on this dataset discriminative dictionary learning is used for the second stage of learning. Finally, defect proneness prediction is conducted on the obtained dictionary. Experiments on the widely used NASA MDP and PROMISE AR datasets indicate the superiority of the proposed algorithm.

Key words： Software Defect Prediction Twice Learning Semi-supervised Learning Dictionary Learning

Received: 28 July 2016

ZTFLH:

TP 311

Fund:Supported by National Natural Science Foundation of China(No.61272273,61073113), Graduate Student Innovation Research Project of Jiangsu Province(No.CXZZ12_0478)

About author:: ZHANG Zhiwu, born in 1981, Ph.D. candidate. His research interests include pa-ttern recognition, machine learning and software engineering.
JING Xiaoyuan(Corresponding author), born in 1971, Ph.D., professor. His research interests include pattern recognition, machine learning and software engineering.
WU Fei, born in 1989, Ph.D.,lecturer. His research interests include pattern recognition, machine learning and software enginee-ring.

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	ZHANG Zhiwu
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Cite this article:

ZHANG Zhiwu,JING Xiaoyuan,WU Fei. Twice Learning Based Semi-supervised Dictionary Learning for Software Defect Prediction[J]. , 2017, 30(3): 242-250.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201703006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I3/242

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