On the Use of Agentic Coding: An Empirical Study of Pull Requests on
  GitHub
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.
68d036918adc5cd018d15a02
Miku Watanabe
68d036918adc5cd018d15a03
Hao Li
claimed_verified
2025-09-23T10:07:21.461Z
62b4f3b7464e664268bf4e85
/avatars/16e79e11a8734b1d241e0f0c55a54045.svg
Leo
user
hao-li
68d036918adc5cd018d15a04
Yutaro Kashiwa
68d036918adc5cd018d15a05
Brittany Reid
68d036918adc5cd018d15a06
Hajimu Iida
68d036918adc5cd018d15a07
Ahmed E. Hassan
On the Use of Agentic Coding: An Empirical Study of Pull Requests on
  GitHub
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.
Agent-assisted pull requests generated by Claude Code are largely accepted in open-source projects, with most requiring minimal human modification.
large language models LLMs autonomous AI agents GitHub pull requests PRs agentic coding tool refactoring documentation testing project maintainers human revisions bug fixes project-specific standards