In this paper, we present ApacheJIT, a large dataset for Just-In-Time defect prediction. ApacheJIT consists of clean and bug-inducing software changes in popular Apache projects. ApacheJIT has a total of 106,674 commits (28,239 bug-inducing and 78,435 clean commits). Having a large number of commits makes ApacheJIT a suitable dataset for machine learning models, especially deep learning models that require large training sets to effectively generalize the patterns present in the historical data to future data.
@article{arxiv.2203.00101,
title = {ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction},
author = {Hossein Keshavarz and Meiyappan Nagappan},
journal= {arXiv preprint arXiv:2203.00101},
year = {2022}
}