2024
DOI: 10.35882/jeeemi.v6i2.409
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Impact of a Synthetic Data Vault for Imbalanced Class in Cross-Project Defect Prediction

Putri Nabella,
Rudy Herteno,
Setyo Wahyu Saputro
et al.

Abstract: Software Defect Prediction (SDP) is crucial for ensuring software quality. However, class imbalance (CI) poses a significant challenge in predictive modeling. This study delves into the effectiveness of the Synthetic Data Vault (SDV) in mitigating CI within Cross-Project Defect Prediction (CPDP). Methodologically, the study addresses CI across ReLink, MDP, and PROMISE datasets by leveraging SDV to augment minority classes. Classification utilizing Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbo… Show more

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