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ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction
February 28, 2022 Β· Declared Dead Β· π IEEE Working Conference on Mining Software Repositories
"No code URL or promise found in abstract"
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Authors
Hossein Keshavarz, Meiyappan Nagappan
arXiv ID
2203.00101
Category
cs.SE: Software Engineering
Cross-listed
cs.AI,
cs.LG,
cs.PL
Citations
25
Venue
IEEE Working Conference on Mining Software Repositories
Last Checked
4 months ago
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.
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