arxiv:2203.00101

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

Published on Apr 30, 2022

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Abstract

ApacheJIT is a large dataset of software changes from Apache projects designed for Just-In-Time defect prediction using machine learning models.

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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.

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