TITLE: ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction 

ABSTRACT: 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.
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.
ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction
http://arxiv.org/abs/2203.00101v2