期刊
IEEE TRANSACTIONS ON RELIABILITY
卷 69, 期 1, 页码 40-54出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2019.2895462
关键词
Training; Learning systems; Software; Measurement; NASA; Predictive models; Machine learning; Class-imbalance; cross-project; ensemble learning; software defect prediction (SDP); within-project
类别
资金
- National Natural Science Foundation of China [61673384, 61502497, 61562015]
Software defect prediction (SDP) is an available way to enhance test efficiency and guarantee software reliability. However, there are more clean instances than defective instances in real software projects, and this results in severe class distribution skews and gets the poor performance of classifiers. So solving the class-imbalance problem in SDP has attracted growing attention from industry and academia in software engineering. In this paper, we propose a novel class-imbalance learning approach for both within-project and cross-project class-imbalance problem. We utilize the thought of stratification embedded in nearest neighbor (STr-NN) to produce evolving training datasets with balanced data. For within-project, we directly employ the STr-NN approach for defect prediction. For cross-project, we first introduce transfer component analysis to mitigate the distribution differences between source and target dataset, and then employ the STr-NN approach on the transferred data. We conduct experiments on PROMISE and NASA datasets using ensemble learning based on weight vote. Experimental results indicate that our approach has higher area under curve (AUC), Recall and comparable probability of a false alarm (pf), and F-measure than some existing methods for the class-imbalance problem.
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