Homogeneous cross-project defect prediction (HCPDP) aims to apply a binary classification model built on source projects to a target project with the same metrics. However, there is still room for improvement in the performance of the existing HCPDP models. This study has proposed a novel approach, including one-to-one and many-to-one predictions. First, we apply the Jensen-Shannon divergence to select the most similar source project automatically. Second, relative density estimation is introduced to choose the suitable instance of the selected source project. Third, one-to-one and many-to-one prediction models are trained by the selected instances. Finally, two benchmark datasets are used to evaluate the proposed approach. Compared to the state-of-the-art methods, the experimental results demonstrated that the proposed approach could improve the prediction performance in the F1-score, AUC, and G-mean metrics and exhibit strong adaptability to the traditional classifiers.