  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.
