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

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 addition to the original dataset, we also present carefully selected training and test sets that we recommend to be used in training and evaluating machine learning models.