Our results show that HYDRA achieves an average F1-score of 0.544. We compare our approach with the most recently proposed cross-project defect prediction approaches: TCA+ by Nam et al., Peters filter by Peters et al., GP by Liu et al., MO by Canfora et al., and CODEP by Panichella et al. We experiment with logistic regression as the underlying classification algorithm of HYDRA. To examine the benefits of HYDRA, we perform experiments on 29 datasets from the PROMISE repository which contains a total of 11,196 instances (i.e., Java classes) labeled as defective or clean. These two phases create a massive composition of classifiers. In this work, we propose a HYbrid moDel Reconstruction Approach (HYDRA) for cross-project defect prediction, which includes two phases: genetic algorithm (GA) phase and ensemble learning (EL) phase. Cross-project defect prediction, which uses data from other projects to predict defects in a particular project, provides a new perspective to defect prediction. However, often a new project does not have enough training data. Most software defect prediction approaches are trained and applied on data from the same project.
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