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

arXiv: https://arxiv.org/abs/2203.00101 · HTML: https://mlsanity.com/paper/2203.00101/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [CC2Vec: Distributed Representations of Code Changes](https://mlsanity.com/paper/2003.05620/) (2020)
- [Do We Need More Training Data?](https://mlsanity.com/paper/1503.01508/) (2015)
- [JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction](https://mlsanity.com/paper/2103.07068/) (2021)
- [Watch out for Extrinsic Bugs! A Case Study of their Impact in Just-In-Time Bug Prediction Models on the OpenStack project](https://mlsanity.com/paper/2103.15180/) (2021)

## Built on by (most cited first, 3 of 3)

- [Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study](https://mlsanity.com/paper/2501.04690/) (2025)
- [On the calibration of Just-in-time Defect Prediction](https://mlsanity.com/paper/2504.12051/) (2025)
- [Deployment Risk Assessment Using Diff-Aware Features: A Case Study at Prime Video](https://mlsanity.com/paper/2607.06766/) (2026)

## Abstract (arXiv)

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.