Investigation of Dataset Features for Just-in-Time Defect Prediction
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Just-in-time (JIT) defect prediction refers to the technique of predicting whether a code change is defective. Many contributions have been made in this area through the excellent dataset by Kamei. In this paper, we revisit the dataset and highlight preprocessing difficulties with the dataset and the limitations of the dataset on unsupervised learning. Secondly, we propose certain features in the Kamei dataset that can be used for training models. Lastly, we discuss the limitations of the dataset's features.
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