On the Use of Agentic Coding: An Empirical Study of Pull Requests on
  GitHub
Authors: name: Miku Watanabe, name: Hao Li, name: Yutaro Kashiwa, name: Brittany Reid, name: Hajimu Iida, name: Ahmed E. Hassan

Large language models (LLMs) are increasingly being integrated into software
development processes. The ability to generate code and submit pull requests
with minimal human intervention, through the use of autonomous AI agents, is
poised to become a standard practice. However, little is known about the
practical usefulness of these pull requests and the extent to which their
contributions are accepted in real-world projects. In this paper, we
empirically study 567 GitHub pull requests (PRs) generated using Claude Code,
an agentic coding tool, across 157 diverse open-source projects. Our analysis
reveals that developers tend to rely on agents for tasks such as refactoring,
documentation, and testing. The results indicate that 83.8% of these
agent-assisted PRs are eventually accepted and merged by project maintainers,
with 54.9% of the merged PRs are integrated without further modification. The
remaining 45.1% require additional changes benefit from human revisions,
especially for bug fixes, documentation, and adherence to project-specific
standards. These findings suggest that while agent-assisted PRs are largely
acceptable, they still benefit from human oversight and refinement.