Twice Learning Based Semi-supervised Dictionary Learning for Software Defect Prediction
ZHANG Zhiwu1, JING Xiaoyuan1,2, WU Fei3
1.School of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023 2.State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072 3.School of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023
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.
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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2017, Vol. 30 
