  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.
