Megadiff: A Dataset of 600k Java Source Code Changes Categorized by Diff Size
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Abstract (arXiv)
This paper presents Megadiff, a dataset of source code diffs. It focuses on Java, with strict inclusion criteria based on commit message and diff size. Megadiff contains 663 029 Java diffs that can be used for research on commit comprehension, fault localization, automated program repair, and machine learning on code changes.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Aug 2021.
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Citing papers per year: 2023: 6; 2024: 1; 2025: 2; 2026: 1.
10 papers.
- 2023OctoPack: Instruction Tuning Code Large Language Modelscited by 189
- 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
- 2023RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repaircited by 50
- 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 15
- 2023RunBugRun -- An Executable Dataset for Automated Program Repaircited by 11
- 2025The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Modelscited by 7
- 2023Out of Context: How important is Local Context in Neural Program Repair?cited by 5
- 2024Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generationcited by 5
- 2023CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Codecited by 0
- 2026On the Impact of Code Comments for Automated Bug-Fixing: An Empirical Studycited by 0
- 2026On the Impact of Code Comments for Automated Bug-Fixing: An Empirical Study30 Jan 2026
- 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence23 Nov 2025
- 2025The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models5 May 2025
- 2024Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generation22 Oct 2024
- 2023RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair25 Dec 2023
- 2023CodeLL: A Lifelong Learning Dataset to Support the Co-Evolution of Data and Language Models of Code20 Dec 2023
- 2023Out of Context: How important is Local Context in Neural Program Repair?8 Dec 2023
- 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code14 Nov 2023
- 2023OctoPack: Instruction Tuning Code Large Language Models14 Aug 2023
- 2023RunBugRun -- An Executable Dataset for Automated Program Repair3 Apr 2023