Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yoshioka, Haruhiko, Monno, Takahiro, Tokumasu, Haruka, Wakamatsu, Taiki, Ota, Yuki, Weeraddana, Nimmi, Matsumoto, Kenichi
Format:	Preprint
Published:	2026
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions
by: Xiao, Tao, et al.
Published: (2024)

How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses
by: Watanabe, Kan, et al.
Published: (2026)

Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study
by: Peralta, Sien Reeve O., et al.
Published: (2026)

On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub
by: Watanabe, Miku, et al.
Published: (2025)

Empirical Analysis of Pull Requests for Google Summer of Code
by: Popoola, Saheed
Published: (2024)

The Value of Effective Pull Request Description
by: Pirouzkhah, Shirin, et al.
Published: (2026)

A Longitudinal Analysis of Good First Issue Practices and Newcomer Pull Requests in Popular OSS Projects
by: Hoshikawa, Hirotatsu, et al.
Published: (2026)

Security in the Age of AI Teammates: An Empirical Study of Agentic Pull Requests on GitHub
by: Siddiq, Mohammed Latif, et al.
Published: (2026)

An Empirical Study on Developers Shared Conversations with ChatGPT in GitHub Pull Requests and Issues
by: Hao, Huizi, et al.
Published: (2024)

How Do Developers Use Code Suggestions in Pull Request Reviews?
by: Bouraffa, Abir, et al.
Published: (2025)

GitHub Actions: The Impact on the Pull Request Process
by: Wessel, Mairieli, et al.
Published: (2022)

Insights into Security-Related AI-Generated Pull Requests
by: Rabbi, Md Fazle, et al.
Published: (2026)

The Impact of Documentation on Test Engagement in Pull Requests in OSS
by: Amore, Teal, et al.
Published: (2026)

From Industry Claims to Empirical Reality: An Empirical Study of Code Review Agents in Pull Requests
by: Chowdhury, Kowshik, et al.
Published: (2026)

On the Footprints of Reviewer Bots Feedback on Agentic Pull Requests in OSS GitHub Repositories
by: Fatima, Syeda Kaneez, et al.
Published: (2026)

The Quiet Contributions: Insights into AI-Generated Silent Pull Requests
by: Hasan, S M Mahedy, et al.
Published: (2026)

A Study of Library Usage in Agent-Authored Pull Requests
by: Twist, Lukas, et al.
Published: (2025)

Do Autonomous Agents Contribute Test Code? A Study of Tests in Agentic Pull Requests
by: Haque, Sabrina, et al.
Published: (2026)

Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub
by: Ehsani, Ramtin, et al.
Published: (2026)

What Developers Ask to ChatGPT in GitHub Pull Requests? an Exploratory Study
by: Silva, Julyanara R., et al.
Published: (2025)

Evaluating the Impact of Data Cleaning on the Quality of Generated Pull Request Descriptions
by: Tire, Kutay, et al.
Published: (2025)

Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requests
by: Pham, Dung, et al.
Published: (2026)

Sphinx: Benchmarking and Modeling for LLM-Driven Pull Request Review
by: Zhang, Daoan, et al.
Published: (2026)

MELT: Mining Effective Lightweight Transformations from Pull Requests
by: Ramos, Daniel, et al.
Published: (2023)

How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study
by: Mitul, Tanjum Motin, et al.
Published: (2025)

How Do Agentic AI Systems Address Performance Optimizations? A BERTopic-Based Analysis of Pull Requests
by: Opu, Md Nahidul Islam, et al.
Published: (2025)

Improving Merge Pipeline Throughput in Continuous Integration via Pull Request Prioritization
by: Jungwirth, Maximilian, et al.
Published: (2025)

Change And Cover: Last-Mile, Pull Request-Based Regression Test Augmentation
by: Zhou, Zitong, et al.
Published: (2026)

Why Are AI Agent Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study
by: Alam, Khairul, et al.
Published: (2026)

Beyond Bug Fixes: An Empirical Investigation of Post-Merge Code Quality Issues in Agent-Generated Pull Requests
by: Cynthia, Shamse Tasnim, et al.
Published: (2026)

Quality and Security Signals in AI-Generated Python Refactoring Pull Requests
by: Almukhtar, Mohamed, et al.
Published: (2026)

Who Writes the Docs in SE 3.0? Agent vs. Human Documentation Pull Requests
by: Yamasaki, Kazuma, et al.
Published: (2026)

Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance
by: Pinna, Giovanni, et al.
Published: (2026)

Humans Integrate, Agents Fix: How Agent-Authored Pull Requests Are Referenced in Practice
by: Khemissi, Islem, et al.
Published: (2026)

Human-Agent versus Human Pull Requests: A Testing-Focused Characterization and Comparison
by: Milanese, Roberto, et al.
Published: (2026)

Quality Gatekeepers: Investigating the Effects ofCode Review Bots on Pull Request Activities
by: Wessel, Mairieli, et al.
Published: (2021)

On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requests
by: Khatoonabadi, SayedHassan, et al.
Published: (2021)

Predicting the First Response Latency of Maintainers and Contributors in Pull Requests
by: Khatoonabadi, SayedHassan, et al.
Published: (2023)

Early-Stage Prediction of Review Effort in AI-Generated Pull Requests
by: Minh, Dao Sy Duy, et al.
Published: (2026)

Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests
by: Gong, Jingzhi, et al.
Published: (2026)