How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses

TL;DR

This paper empirically analyzes how AI coding agents differ in their pull request descriptions and how human reviewers respond, revealing that description styles influence reviewer engagement and merge outcomes in AI-assisted development.

Contribution

It provides the first detailed empirical comparison of AI coding agents' pull request styles and their impact on human review responses and project outcomes.

Findings

AI agents have distinct PR description styles.

Description styles affect reviewer engagement and response times.

Variation exists in merge rates across different AI agents.

Abstract

The rapid adoption of large language models has led to the emergence of AI coding agents that autonomously create pull requests on GitHub. However, how these agents differ in their pull request description characteristics, and how human reviewers respond to them, remains underexplored. In this study, we conduct an empirical analysis of pull requests created by five AI coding agents using the AIDev dataset. We analyze agent differences in pull request description characteristics, including structural features, and examine human reviewer response in terms of review activity, response timing, sentiment, and merge outcomes. We find that AI coding agents exhibit distinct PR description styles, which are associated with differences in reviewer engagement, response time, and merge outcomes. We observe notable variation across agents in both reviewer interaction metrics and merge rates. These findings highlight the role of pull request presentation and reviewer interaction dynamics in human-AI collaborative software development.