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Heterogeneous defect prediction (HDP) aims to predict defect-prone software modules in one project using heterogeneous data collected from other projects. Recently, several HDP methods have been proposed. However, these methods do not sufficiently incorporate the two characteristics of the defect prediction data: (1) data could be linearly inseparable, and (2) data could be highly imbalanced. These...
When there exists not enough historical defect data for building accurate prediction model, semi-supervised defect prediction (SSDP) and cross-project defect prediction (CPDP) are two feasible solutions. Existing CPDP methods assume that the available source data is well labeled. However, due to expensive human efforts for labeling a large amount of defect data, usually, we can only make use of the...
In this correspondence, we point out a discrepancy in a recent paper, "data mining static code attributes to learn defect predictors," that was published in this journal. Because of the small percentage of defective modules, using probability of detection (pd) and probability of false alarm (pf) as accuracy measures may lead to impractical prediction models.
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