ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction
This provides a resource for researchers and practitioners in software engineering to improve defect prediction models, though it is incremental as it focuses on dataset creation rather than new methods.
The paper introduces ApacheJIT, a large dataset of 106,674 commits (28,239 bug-inducing and 78,435 clean) from Apache projects, to address the need for extensive data in Just-In-Time defect prediction, enabling better training for machine learning models.
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.