Semi-supervised Software Defect Prediction Using Task-Driven Dictionary Learning  

作　　者：CHENG Ming WU Guoqing YUAN Mengting WAN Hongyan 

机构地区：[1]School of Computer, Wuhan University, Wuhan 430072, China [2]State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China

出　　处：《Chinese Journal of Electronics》2016年第6期1089-1096,共8页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.91118003,No.61170022,No.61003071)

摘　　要：We present a semi-supervised approach for software defect prediction. The proposed method is designed to address the special problematic characteristics of software defect datasets, namely, lack of labeled samples and class-imbalanced data. To alleviate these problems, the proposed method features the following components. Being a semi-supervised approach, it exploits the wealth of unlabeled samples in software systems by evaluating the confidence probability of the predicted labels, for each unlabeled sample. And we propose to jointly optimize the classifier parameters and the dictionary by a task-driven formulation, to ensure that the learned features(sparse code) are optimal for the trained classifier. Finally, during the dictionary learning process we take the different misclassification costs into consideration to improve the prediction performance. Experimental results demonstrate that our method outperforms several representative stateof-the-art defect prediction methods.We present a semi-supervised approach for software defect prediction. The proposed method is designed to address the special problematic characteristics of software defect datasets, namely, lack of labeled samples and class-imbalanced data. To alleviate these problems, the proposed method features the following components. Being a semi-supervised approach, it exploits the wealth of unlabeled samples in software systems by evaluating the confidence probability of the predicted labels, for each unlabeled sample. And we propose to jointly optimize the classifier parameters and the dictionary by a task-driven formulation, to ensure that the learned features（sparse code） are optimal for the trained classifier. Finally, during the dictionary learning process we take the different misclassification costs into consideration to improve the prediction performance. Experimental results demonstrate that our method outperforms several representative stateof-the-art defect prediction methods.

关 键 词：Software defect prediction Task-driven dictionary learning Cost-sensitive Semi-supervised learning Sparse representation 

分 类 号：TP311.53[自动化与计算机技术—计算机软件与理论]