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Just-in-time (JIT) software defect prediction aims to predict whether code commits during project development and maintenance will introduce defects.In the field of JIT software defect prediction research,model training relies on high-quality datasets.However,the impact of dataset augmentation methods on JIT software defect prediction has not been thoroughly investigated in existing methods.To enhance the performance of JIT software defect prediction,a method based on dataset augmentation,named prediction based on data augmentation (PDA) is proposed.PDA includes four parts:feature stitching,sample generation,sample filtering,and sampling processing.The augmented dataset has an ample number of samples with high quality and eliminates the class imbalance problem.Comparing the proposed PDA method with the latest JIT software defect prediction method (JIT-Fine),results indicate:an 18.33%improvement in the F_1score on the JIT-Defects4J dataset;and a 3.67%improvement on the LLTC4J dataset,demonstrating PDA′s generalization ability.Ablation studies have confirmed that the performance improvement of the proposed PDA method mainly comes from dataset augmentation and filtering mechanisms.
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Basic Information:
DOI：10.12194/j.ntu.20231206001
China Classification Code:TP311.5
Citation Information:
[1]YANG Fan,XIA Hongling.A just-in-time software defect prediction method based on data augmentation[J].Journal of Nantong University (Natural Science Edition),2024,23(01):58-65.DOI:10.12194/j.ntu.20231206001.
Fund Information:
南通市科技计划面上项目（JC2023070）
2024-01-09
2024-01-09
2024-01-09