Copertina

When AI Teammates Meet Code Review: Collaboration Signals Shaping the Integration of Agent-Authored Pull Requests

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Nachuma, Costain, Zibran, Minhaz
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Software Engineering Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Decoding Dependency Risks: A Quantitative Study of Vulnerabilities in the Maven Ecosystem
di: Nachuma, Costain, et al.
Pubblicazione: (2025)

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

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

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

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

SWE-PRBench: Benchmarking AI Code Review Quality Against Pull Request Feedback
di: Kumar, Deepak
Pubblicazione: (2026)

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

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

Pull Requests as a Training Signal for Repo-Level Code Editing
di: Zhu, Qinglin, et al.
Pubblicazione: (2026)

A Task-Level Evaluation of AI Agents in Open-Source Projects
di: Rahman, Shojibur, et al.
Pubblicazione: (2026)

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

More Code, Less Reuse: Investigating Code Quality and Reviewer Sentiment towards AI-generated Pull Requests
di: Huang, Haoming, et al.
Pubblicazione: (2026)

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

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

How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests
di: Ogenrwot, Daniel, et al.
Pubblicazione: (2026)

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

Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles
di: Jo, Young, et al.
Pubblicazione: (2026)

On Unified Prompt Tuning for Request Quality Assurance in Public Code Review
di: Chen, Xinyu, et al.
Pubblicazione: (2024)

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

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

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

Welcome Your New AI Teammate: On Safety Analysis by Leashing Large Language Models
di: Nouri, Ali, et al.
Pubblicazione: (2024)

Knowledge-Guided Prompt Learning for Request Quality Assurance in Public Code Review
di: Li, Lin, et al.
Pubblicazione: (2024)

Multi-line AI-assisted Code Authoring
di: Dunay, Omer, et al.
Pubblicazione: (2024)

The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering
di: Li, Hao, et al.
Pubblicazione: (2025)

Architecture Without Architects: How AI Coding Agents Shape Software Architecture
di: Konrad, Phongsakon Mark, et al.
Pubblicazione: (2026)

On Assessing the Relevance of Code Reviews Authored by Generative Models
di: Heumüller, Robert, et al.
Pubblicazione: (2025)

On The Impact of Merge Request Deviations on Code Review Practices
di: Kansab, Samah, et al.
Pubblicazione: (2025)

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

Code Review Agent Benchmark
di: Zhang, Yuntong, et al.
Pubblicazione: (2026)

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

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

Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review
di: Kamalı, Hüseyin Özgür, et al.
Pubblicazione: (2026)

AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluation
di: Murali, Vijayaraghavan, et al.
Pubblicazione: (2023)

When Fuzzing Meets LLMs: Challenges and Opportunities
di: Jiang, Yu, et al.
Pubblicazione: (2024)

AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub
di: Ogenrwot, Daniel, et al.
Pubblicazione: (2026)

AI-Assisted Assessment of Coding Practices in Modern Code Review
di: Vijayvergiya, Manushree, et al.
Pubblicazione: (2024)

When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents
di: Yan, Lu, et al.
Pubblicazione: (2026)

Adaptive Confidence Gating in Multi-Agent Collaboration for Efficient and Optimized Code Generation
di: Zhang, Haoji, et al.
Pubblicazione: (2026)

Learning to Ask: When LLM Agents Meet Unclear Instruction
di: Wang, Wenxuan, et al.
Pubblicazione: (2024)