An Empirical Comparison of Resampling Ensemble Methods of Deep Learning Neural Networks for Cross-Project Software Defect Prediction
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DOI: 10.22266/ijies2021.0630.18
Abstract: Software defect prediction is one of the most important quality assurance activities during software development. This paper contributes empirical insights into the effectiveness of three resampling ensemble methods (bagging, boosting, and dagging) of Deep Learning… read more here.
Keywords: software defect; software; ensemble methods; defect prediction ... See more keywords