ApacheJIT
Hossein Keshavarz, Meiyappan Nagappan
Abstract
In this paper, we present ApacheJIT, a large dataset for Just-In-Time (JIT) defect prediction. ApacheJIT consists of clean and bug-inducing software changes in 14 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 JIT models, especially deep learning models that require large training sets to effectively generalize the patterns present in the historical data to future data.
Topics & Concepts
Computer scienceSoftwareSoftware bugTraining setArtificial intelligenceData modelingSoftware engineeringMachine learningData miningProgramming languageSoftware Engineering ResearchMachine Learning and Data ClassificationSoftware Reliability and Analysis Research