AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub
Summary: arXiv:2604.03551v1 Announce Type: cross
Abstract
Software Engineering 3.0 marks a paradigm shift in software development, in which AI coding agents are no longer just assistive tools but active contributors. While prior empirical studies have examined productivity gains and acceptance patterns in AI-assisted development, the challenges associated with integrating agent-generated contributions remain less understood. In particular, merge conflicts, a fundamental aspect of collaborative software development, remain underexplored in this context.
In this paper, we present AgenticFlict, a large-scale dataset of textual merge conflicts in AI coding agent pull requests (Agentic PRs). The dataset comprises over 142,000 Agentic PRs collected from more than 59,000 repositories, of which over 107,000 have been successfully processed through deterministic merge simulation.
Key Findings
Our pipeline identifies more than 29,000 PRs exhibiting merge conflicts, yielding a conflict rate of 27.67%. We also extracted over 336,000 fine-grained conflict regions across these instances.
Significance of the Dataset
Our preliminary exploratory analysis indicates that merge conflicts are both frequent and often substantial in AI-generated contributions, with noticeable variation across different agents. This emphasizes the need to better understand and manage integration challenges in AI-assisted software development.
Dataset and Resources
The dataset, code, and supplementary materials are available at the following link:
Conclusion
The emergence of AI coding agents in software development presents both opportunities and challenges. As these agents become integral to the coding process, understanding the dynamics of merge conflicts will be crucial for improving collaboration and enhancing productivity in software engineering. Future research should focus on devising strategies to mitigate these conflicts and improve the integration of AI-generated code into existing projects.