AgentPack: A Dataset of Code Changes, Co-Authored by Agents and Humans

• URL: http://arxiv.org/abs/2509.21891v1
• Date: Fri, 26 Sep 2025 05:28:22 GMT
• Title: AgentPack: A Dataset of Code Changes, Co-Authored by Agents and Humans
• Authors: Yangtian Zi, Zixuan Wu, Aleksander Boruch-Gruszecki, Jonathan Bell, Arjun Guha,
• Abstract summary: Fine-tuning large language models for code editing has typically relied on mining commits and pull requests.<n>We present AgentPack, a corpus of 1.3M code edits co-authored by Claude Code, OpenAI Codex, and Cursor Agent.<n>We show that models fine-tuned on AgentPack can outperform models trained on prior human-only commit corpora.
• Score: 46.56091965723774
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Fine-tuning large language models for code editing has typically relied on mining commits and pull requests. The working hypothesis has been that commit messages describe human intent in natural language, and patches to code describe the changes that implement that intent. However, much of the previously collected data is noisy: commit messages are terse, human-written commits commingle several unrelated edits, and many commits come from simple, rule-based bots. The recent adoption of software engineering agents changes this landscape. Code changes co-authored by humans and agents tend to be more narrowly scoped and focused on clearer goals. Their commit messages, generated by LLMs, articulate intent and rationale in much greater detail. Moreover, when these changes land in public repositories, they are implicitly filtered by humans: maintainers discard low-quality commits to their projects. We present AgentPack, a corpus of 1.3M code edits co-authored by Claude Code, OpenAI Codex, and Cursor Agent across public GitHub projects up to mid-August 2025. We describe the identification and curation pipeline, quantify adoption trends of these agents, and analyze the structural properties of the edits. Finally, we show that models fine-tuned on AgentPack can outperform models trained on prior human-only commit corpora, highlighting the potential of using public data from software engineering agents to train future code-editing models.

Related papers

• Agentic Much? Adoption of Coding Agents on GitHub [6.395990525268647]
  We present the first large-scale study of the adoption of coding agents on GitHub.<n>We find an estimated adoption rate of 15.85%--22.60%, which is very high for a technology only a few months old--and increasing.<n>At the commit level, we find that commits assisted by coding agents are larger than commits only authored by human developers.
  arXiv  Detail & Related papers  (2026-01-26T10:28:10Z)
• Brevity is the Soul of Wit: Condensing Code Changes to Improve Commit Message Generation [21.625755841132733]
  We propose an alternative way to condense code changes before generation.<n>We first condense code changes by using our proposed templates with the help of a tool named ChangeScribe.<n>Our approach can outperform six baselines in terms of BLEU-Norm, METEOR, and ROUGE-L.
  arXiv  Detail & Related papers  (2025-09-19T04:04:28Z)
• Evaluating Software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios [13.949319911378826]
  This study evaluated 4,892 patches from 10 top-ranked agents on 500 real-world GitHub issues.<n>No single agent dominated, with 170 issues unresolved, indicating room for improvement.<n>Most agents maintained code reliability and security, avoiding new bugs or vulnerabilities.<n>Some agents increased code complexity, many reduced code duplication and minimized code smells.
  arXiv  Detail & Related papers  (2024-10-16T11:33:57Z)
• AgentGym: Evolving Large Language Model-based Agents across Diverse Environments [116.97648507802926]
  Large language models (LLMs) are considered a promising foundation to build such agents. We take the first step towards building generally-capable LLM-based agents with self-evolution ability. We propose AgentGym, a new framework featuring a variety of environments and tasks for broad, real-time, uni-format, and concurrent agent exploration.
  arXiv  Detail & Related papers  (2024-06-06T15:15:41Z)
• Executable Code Actions Elicit Better LLM Agents [76.95566120678787]
  This work proposes to use Python code to consolidate Large Language Model (LLM) agents' actions into a unified action space (CodeAct) integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations through multi-turn interactions. The encouraging performance of CodeAct motivates us to build an open-source LLM agent that interacts with environments by executing interpretable code and collaborates with users using natural language.
  arXiv  Detail & Related papers  (2024-02-01T21:38:58Z)
• Commit Messages in the Age of Large Language Models [0.9217021281095906]
  We evaluate the performance of OpenAI's ChatGPT for generating commit messages based on code changes. We compare the results obtained with ChatGPT to previous automatic commit message generation methods that have been trained specifically on commit data.
  arXiv  Detail & Related papers  (2024-01-31T06:47:12Z)
• InstructCoder: Instruction Tuning Large Language Models for Code Editing [26.160498475809266]
  We explore the use of Large Language Models (LLMs) to edit code based on user instructions. InstructCoder is the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing. Our findings reveal that open-source LLMs fine-tuned on InstructCoder can significantly enhance the accuracy of code edits.
  arXiv  Detail & Related papers  (2023-10-31T10:15:35Z)
• Agents: An Open-source Framework for Autonomous Language Agents [98.91085725608917]
  We consider language agents as a promising direction towards artificial general intelligence. We release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience.
  arXiv  Detail & Related papers  (2023-09-14T17:18:25Z)
• Delving into Commit-Issue Correlation to Enhance Commit Message Generation Models [13.605167159285374]
  Commit message generation is a challenging task in automated software engineering. tool is a novel paradigm that can introduce the correlation between commits and issues into the training phase of models. The results show that compared with the original models, the performance of tool-enhanced models is significantly improved.
  arXiv  Detail & Related papers  (2023-07-31T20:35:00Z)
• Coeditor: Leveraging Contextual Changes for Multi-round Code Auto-editing [57.776971051512234]
  In this work, we explore a multi-round code auto-editing setting, aiming to predict edits to a code region based on recent changes within the same. Our model, Coeditor, is a fine-tuned language model specifically designed for code editing tasks. In a simplified single-round, single-edit task, Coeditor significantly outperforms GPT-3.5 and SOTA open-source code completion models.
  arXiv  Detail & Related papers  (2023-05-29T19:57:36Z)
• Using Developer Discussions to Guide Fixing Bugs in Software [51.00904399653609]
  We propose using bug report discussions, which are available before the task is performed and are also naturally occurring, avoiding the need for additional information from developers. We demonstrate that various forms of natural language context derived from such discussions can aid bug-fixing, even leading to improved performance over using commit messages corresponding to the oracle bug-fixing commits.
  arXiv  Detail & Related papers  (2022-11-11T16:37:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.