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• W2401031807 abstract "Cross-project defect prediction (CPDP) plays an important role in estimating the most likely defect-prone software components, especially for new or inactive projects. To the best of our knowledge, few prior studies provide explicit guidelines on how to select suitable training data of quality from a large number of public software repositories. In this paper, we have proposed a training data simplification method for practical CPDP in consideration of multiple levels of granularity and filtering strategies for data sets. In addition, we have also provided quantitative evidence on the selection of a suitable filter in terms of defect-proneness ratio. Based on an empirical study on 34 releases of 10 open-source projects, we have elaborately compared the prediction performance of different defect predictors built with five well-known classifiers using training data simplified at different levels of granularity and with two popular filters. The results indicate that when using the multi-granularity simplification method with an appropriate filter, the prediction models based on Naive Bayes can achieve fairly good performance and outperform the benchmark method." @default.
• W2401031807 created "2016-06-24" @default.
• W2401031807 creator A5009574695 @default.
• W2401031807 creator A5026774161 @default.
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• W2401031807 creator A5056566237 @default.
• W2401031807 date "2014-05-05" @default.
• W2401031807 modified "2023-09-28" @default.
• W2401031807 title "Simplification of Training Data for Cross-Project Defect Prediction." @default.
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• W2401031807 hasPublicationYear "2014" @default.
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