An Approach to Improving Homogeneous Cross-Project Defect Prediction by Jensen-Shannon Divergence and Relative Density

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,...

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Bibliographic Details
Published in:Scientific programming 2022-10, Vol.2022, p.1-16
Main Authors: Ren, Jiajia, Peng, Chunyu, Zheng, Shang, Zou, Haitao, Gao, Shang
Format: Article
Language:English
Subjects:
Algorithms
Defects
Density
Methods
Prediction models
Software
Specific gravity
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Summary: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.
ISSN:1058-9244
1875-919X
DOI:10.1155/2022/4648468