Impact of the Distribution Parameter of Data Sampling Approaches on Software Defect Prediction Models

Bennin, Kwabena Ebo; Keung, Jacky; Monden, Akito

doi:10.1109/apsec.2017.76

Cited by 2 publications

(2 citation statements)

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“…They conducted experiments on 10 open source projects and the results found that the investigated sampling methods had significant and practical effects in terms of performance indicators Pd, Pf, and G-mean but had no impact on AUC. Bennin et al [31] empirically assessed the impacts of the percentage of fault-prone modules on seven sampling methods applied to five classification models for defect prediction. They conducted experiments on 10 static metric projects and the results demonstrated that the performance of these classification models could be largely impacted by this parameter except for AUC.…”

Section: Class Imbalanced Learning In Defect Predictionmentioning

confidence: 99%

“…Bennin et al. [31] empirically assessed the impacts of the percentage of fault‐prone modules on seven sampling methods applied to five classification models for defect prediction. They conducted experiments on 10 static metric projects and the results demonstrated that the performance of these classification models could be largely impacted by this parameter except for AUC.…”

Section: Related Workmentioning

confidence: 99%

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A compositional model for effort‐aware Just‐In‐Time defect prediction on android apps

et al. 2021

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Android apps have played important roles in daily life and work. To meet the new requirements from users, the apps encounter frequent updates, which involves a large quantity of code commits. Previous studies proposed to apply Just‐in‐Time (JIT) defect prediction for apps to timely identify whether the new code commits can introduce defects into apps, aiming to assure their quality. In general, high‐quality features are benefits for improving the classification performance. In addition, the number of defective commit instances is much fewer than that of clean ones, that is the defect data is class imbalanced. In this study, a novel compositional model, called KPIDL, is proposed to conduct the JIT defect prediction task for Android apps. More specifically, KPIDL first exploits a feature learning technique to preprocess original data for obtaining better feature representation, and then introduces a state‐of‐the‐art cost‐sensitive cross‐entropy loss function into the deep neural network to alleviate the class imbalance issue by considering the prior probability of the two types of classes. The experiments were conducted on a benchmark defect data consisting of 15 Android apps. The experimental results show that the proposed KPIDL model performs significantly better than 25 comparative methods in terms of two effort‐aware performance indicators in most cases.

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Section: Class Imbalanced Learning In Defect Predictionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%