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Software defect prediction can classify new software entities into either buggy or clean. However the effectiveness of existing methods is influenced by irrelevant and redundant features. In this paper, we propose a new feature selection framework FECAR using Feature Clustering And feature Ranking. This framework firstly partitions original features into k(More)
Noises are inevitable when mining software archives for software fault prediction. Although some researchers have investigated the noise tolerance of existing feature selection methods, few studies focus on proposing new feature selection methods with a certain noise tolerance. To solve this issue, we propose a novel method FECS (FEature Clustering with(More)
Software fault prediction is valuable in predicting fault proneness of software modules and then limited test resources can be effectively allocated for software quality assurance. Researchers have proved that either feature selection or instance reduction can improve the performance of classification models used for fault prediction. However, to the best(More)
Cross project defect prediction (CPDP) is a challenging task since the predictor built on the source projects can hardly generalize well to the target project. Previous studies have shown that both feature mapping and feature selection can alleviate the differences between the source and target projects. In this paper, we propose a novel method FeSCH(More)
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