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ZENODO
Dataset . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub

Authors: Ogenrwot, Daniel; Businge, John;

AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub

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, have received limited attention in this emerging 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 142K+ Agentic PRs collected from 59K+ repositories, of which 107K+ are successfully processed through deterministic merge simulation. Our pipeline identifies 29K+ PRs exhibiting merge conflicts, yielding a conflict rate of 27.67%, and extracts 336K+ fine-grained conflict regions across these instances. Our preliminary exploratory analysis indicates that merge conflicts are both frequent and often substantial in AI-generated contributions, with noticeable variation across agents, emphasizing the need to better understand and manage integration challenges in AI-assisted software development.

If you use AgenticFlict in your research, please cite both the dataset and the companion paper: @dataset{agenticflict2026, title = {AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub}, author = {Ogenrwot, Daniel and Businge, John}, year = {2026}, publisher = {Zenodo}, doi = {10.5281/zenodo.19396916}, url = {https://doi.org/10.5281/zenodo.19396916} } @article{agenticflict2026paper, title = {AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub}, author = {Ogenrwot, Daniel and Businge, John}, year = {2026}, journal = {arXiv preprint arXiv:2604.03551}, url = {https://arxiv.org/abs/2604.03551} } Take a look at our GitHub repository for the code used to construct this dataset and its complete documentation and sample analysis notebooks: https://github.com/unlv-evol/AgenticFlict

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average