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

TL;DR

ApacheJIT is a comprehensive dataset of over 106,000 commits from Apache projects, designed to facilitate machine learning-based defect prediction, especially for deep learning models requiring large training data.

Contribution

The paper introduces ApacheJIT, a large, curated dataset of software commits for improving Just-In-Time defect prediction research.

Findings

Contains 106,674 commits including bug-inducing and clean changes

Suitable for training deep learning models in defect prediction

Enables large-scale empirical studies in software defect prediction

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.