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- Title
An Approach to Improving Homogeneous Cross-Project Defect Prediction by Jensen-Shannon Divergence and Relative Density.
- Authors
Ren, Jiajia; Peng, Chunyu; Zheng, Shang; Zou, Haitao; Gao, Shang
- Abstract
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.
- Subjects
SPECIFIC gravity; PREDICTION models; FORECASTING; CONSTRUCTION projects
- Publication
Scientific Programming, 2022, p1
- ISSN
1058-9244
- Publication type
Article
- DOI
10.1155/2022/4648468