mlsanityData to 7 Oct 2026

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

arXiv 2203.00101 ↗First posted 28 Feb 2022cs.SE cs.AI cs.LGcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 34 references that are in the map. First posted on arXiv 28 Feb 2022.

Builds on

4 of 34 references.

References · 4

  1. 2020CC2Vec: Distributed Representations of Code Changescited by 38
  2. 2015Do We Need More Training Data?cited by 26
  3. 2021JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Predictioncited by 14
  4. 2021Watch out for Extrinsic Bugs! A Case Study of their Impact in Just-In-Time Bug Prediction Models on the OpenStack projectcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Studycited by 1
  2. 2025On the calibration of Just-in-time Defect Predictioncited by 0
  3. 2026Deployment Risk Assessment Using Diff-Aware Features: A Case Study at Prime Videocited by 0