文章基本信息

标题：A REVIEW OF TRAINING DATA SELECTION IN SOFTWARE DEFECT PREDICTION
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作者：BENYAMIN LANGGU SINAGA ; SABRINA AHMAD ; ZURAIDA ABAL ABAS 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2020
卷号：98
期号：12
页码：2092-2108
出版社：Journal of Theoretical and Applied
摘要：The publicly available dataset poses a challenge in selecting the suitable data to train a defect prediction model to predict defect on other projects. Using a cross-project training dataset without a careful selection will degrade the defect prediction performance. Consequently, training data selection is an essential step to develop a defect prediction model. This paper aims to synthesize the state-of-the-art for training data selection methods published from 2009 to 2019. The existing approaches addressing the training data selection issue fall into three groups, which are nearest neighbour, cluster-based, and evolutionary method. According to the results in the literature, the cluster-based method tends to outperform the nearest neighbour method. On the other hand, the research on evolutionary techniques gives promising results but is still scarce. Therefore, the review concludes that there is still some open area for further investigation in training data selection. We also present research direction within this area.
关键词：Software Defect Prediction;Training Data Selection;Nearest-Neighbor;Cluster-based;Evolutionary-based