基于半监督集成学习的软件缺陷预测<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201707007

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Abstract The software defect prediction is usually adversely affected by the limitation of the labeled modules and the class-imbalance of software defect data. Aiming at this problem, a semi-supervised ensemble learning software defect prediction approach is proposed. High-performance classifiers can be built through semi-supervised ensemble learning by using a large amount of unlabeled modules and a better prediction capability is achieved for class-imbalanced data by using a series of weak classifiers to reduce the bias generated by the majority class. With the consideration of the cost of risk in software defect prediction, a sample weight vector updating strategy is employed to reduce the cost of risk caused by misclassifying defective modules as non-defective ones. Experimental results on NASA MDP datasets show better software defect prediction capability of the proposed approach.

Key words： Software Defect Prediction Class-Imbalance Semi-supervised Learning Ensemble Learning

Received: 20 October 2016

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61272273)

Corresponding Authors: (JING Xiaoyuan(Corresponding author), born in 1971, Ph.D., professor. His research interests include pattern recognition, machine learning and software engineering.)

About author:: (WANG Tiejian, born in 1982, Ph.D. candidate. His research interests include pa-ttern 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.)
(JING Xiaoyuan(Corresponding author), born in 1971, Ph.D., professor. His research interests include pattern recognition, machine learning and software engineering.)

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Cite this article:

WANG Tiejian,WU Fei,JING Xiaoyuan. Semi-supervised Ensemble Learning Based Software Defect Prediction[J]. , 2017, 30(7): 646-652.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201707007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I7/646

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