# Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests

arXiv: https://arxiv.org/abs/2601.18749 · HTML: https://mlsanity.com/paper/2601.18749/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 4 of 4)

- [Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity](https://mlsanity.com/paper/2508.21634/) (2025)
- [Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers](https://mlsanity.com/paper/2404.10225/) (2024)
- [DeputyDev -- AI Powered Developer Assistant: Breaking the Code Review Logjam through Contextual AI to Boost Developer Productivity](https://mlsanity.com/paper/2508.09676/) (2025)
- [Ecosystem-wide influences on pull request decisions: insights from NPM](https://mlsanity.com/paper/2410.14695/) (2024)

## Built on by (most cited first, 2 of 2)

- [Early Adoption of Agentic Coding Tools by GitHub Projects](https://mlsanity.com/paper/2607.14037/) (2026)
- [Co-Modifying Application Code and Deployment Infrastructure: A Causal Study of AI Agent Pull Requests](https://mlsanity.com/paper/2610.12849/) (2026)

## Abstract (arXiv)

The automatic generation of pull requests (PRs) using AI agents has become increasingly common. Although AI-generated PRs are fast and easy to create, their merge rates have been reported to be lower than those created by humans. In this study, we conduct a large-scale empirical analysis of 40,214 PRs collected from the AIDev dataset. We extract 64 features across six families and fit statistical regression models to compare PR merge outcomes for human and agentic PRs, as well as across three AI agents. Our results show that submitter attributes dominate merge outcomes for both groups, while review-related features exhibit contrasting effects between human and agentic PRs. The findings of this study provide insights into improving PR quality through human-AI collaboration.