Semi-supervised Ensemble Learning Based Software Defect Prediction
WANG Tiejian1, WU Fei2, JING Xiaoyuan1
1.State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan 430072 2.School of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023
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.
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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2017, Vol. 30 
