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所属单位：计算机科学与技术学院/人工智能学院/软件学院

发表刊物：IEEE ACCESS

关键字：Software quality assurance cross-project defect prediction data filter machine learning

摘要：Cross-project defect prediction (CPDP) is a field of study where a software project lacking enough local data can use data from other projects to build defect predictors. To support CPDP, the cross-project data must be carefully filtered before being applied locally. Researchers have devised and implemented a plethora of various data filters for the improvement of CPDP performance. However, it is still unclear what data filter strategy is most effective, both generally and specifically, in CPDP. The objective of this paper is to provide an extensive comparison of well-known data filters and a novel filter devised in this paper. We perform experiments on 44 releases of 14 open-source projects, and use Naive Bayes and a support vector machine as the underlying classifier. The results demonstrate that the data filter strategy improves the performance of cross-project defect prediction significantly, and the hierarchical select-based filter proposed performs significantly better. Moreover, when using appropriate data filter strategy, the defect predictor built from cross-project data can outperform the predictor learned by using within-project data.

ISSN号：2169-3536

是否译文：否

发表时间：2017-01-01

合写作者：黄志球,王永亮,Fang, Bingwu

通讯作者：李勇