The Quiet Contributions: Insights into AI-Generated Silent Pull Requests
Fuente:
arXiv
Saved in:
| Main Authors: | Hasan, S M Mahedy, Rabbi, Md Fazle, Zibran, Minhaz |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Insights into Security-Related AI-Generated Pull Requests
by: Rabbi, Md Fazle, et al.
Published: (2026)
by: Rabbi, Md Fazle, et al.
Published: (2026)
Insights into Dependency Maintenance Trends in the Maven Ecosystem
by: Chowdhury, Barisha, et al.
Published: (2025)
by: Chowdhury, Barisha, et al.
Published: (2025)
Faster Releases, Fewer Risks: A Study on Maven Artifact Vulnerabilities and Lifecycle Management
by: Shafin, Md Shafiullah, et al.
Published: (2025)
by: Shafin, Md Shafiullah, et al.
Published: (2025)
A Task-Level Evaluation of AI Agents in Open-Source Projects
by: Rahman, Shojibur, et al.
Published: (2026)
by: Rahman, Shojibur, et al.
Published: (2026)
When AI Teammates Meet Code Review: Collaboration Signals Shaping the Integration of Agent-Authored Pull Requests
by: Nachuma, Costain, et al.
Published: (2026)
by: Nachuma, Costain, et al.
Published: (2026)
Chasing the Clock: How Fast Are Vulnerabilities Fixed in the Maven Ecosystem?
by: Rabbi, Md Fazle, et al.
Published: (2025)
by: Rabbi, Md Fazle, et al.
Published: (2025)
Understanding Software Vulnerabilities in the Maven Ecosystem: Patterns, Timelines, and Risks
by: Rabbi, Md Fazle, et al.
Published: (2025)
by: Rabbi, Md Fazle, et al.
Published: (2025)
HEJ-Robust: A Robustness Benchmark for LLM-Based Automated Program Repair
by: Rabbi, Fazle, et al.
Published: (2026)
by: Rabbi, Fazle, et al.
Published: (2026)
Decoding Dependency Risks: A Quantitative Study of Vulnerabilities in the Maven Ecosystem
by: Nachuma, Costain, et al.
Published: (2025)
by: Nachuma, Costain, et al.
Published: (2025)
A Multi-Language Perspective on the Robustness of LLM Code Generation
by: Rabbi, Fazle, et al.
Published: (2025)
by: Rabbi, Fazle, et al.
Published: (2025)
Bias Unveiled: Investigating Social Bias in LLM-Generated Code
by: Ling, Lin, et al.
Published: (2024)
by: Ling, Lin, et al.
Published: (2024)
Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions
by: Xiao, Tao, et al.
Published: (2024)
by: Xiao, Tao, et al.
Published: (2024)
Beyond Translation Accuracy: Addressing False Failures in LLM-Based Code Translation
by: Rabbi, Fazle, et al.
Published: (2026)
by: Rabbi, Fazle, et al.
Published: (2026)
Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests
by: Yoshioka, Haruhiko, et al.
Published: (2026)
by: Yoshioka, Haruhiko, et al.
Published: (2026)
How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study
by: Mitul, Tanjum Motin, et al.
Published: (2025)
by: Mitul, Tanjum Motin, et al.
Published: (2025)
Secure-Instruct: An Automated Pipeline for Synthesizing Instruction-Tuning Datasets Using LLMs for Secure Code Generation
by: Li, Junjie, et al.
Published: (2025)
by: Li, Junjie, et al.
Published: (2025)
The Value of Effective Pull Request Description
by: Pirouzkhah, Shirin, et al.
Published: (2026)
by: Pirouzkhah, Shirin, et al.
Published: (2026)
Do Autonomous Agents Contribute Test Code? A Study of Tests in Agentic Pull Requests
by: Haque, Sabrina, et al.
Published: (2026)
by: Haque, Sabrina, et al.
Published: (2026)
How Do Agentic AI Systems Address Performance Optimizations? A BERTopic-Based Analysis of Pull Requests
by: Opu, Md Nahidul Islam, et al.
Published: (2025)
by: Opu, Md Nahidul Islam, et al.
Published: (2025)
Quality and Security Signals in AI-Generated Python Refactoring Pull Requests
by: Almukhtar, Mohamed, et al.
Published: (2026)
by: Almukhtar, Mohamed, et al.
Published: (2026)
These Aren't the Reviews You're Looking For How Humans Review AI-Generated Pull Requests
by: Duma, Kacper, et al.
Published: (2026)
by: Duma, Kacper, et al.
Published: (2026)
Specification-Driven Code Translation Powered by Large Language Models: How Far Are We?
by: Saha, Soumit Kanti, et al.
Published: (2024)
by: Saha, Soumit Kanti, et al.
Published: (2024)
Automatic Pull Request Description Generation Using LLMs: A T5 Model Approach
by: Sakib, Md Nazmus, et al.
Published: (2024)
by: Sakib, Md Nazmus, et al.
Published: (2024)
On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requests
by: Khatoonabadi, SayedHassan, et al.
Published: (2021)
by: Khatoonabadi, SayedHassan, et al.
Published: (2021)
Evaluating the Impact of Data Cleaning on the Quality of Generated Pull Request Descriptions
by: Tire, Kutay, et al.
Published: (2025)
by: Tire, Kutay, et al.
Published: (2025)
A Study of Library Usage in Agent-Authored Pull Requests
by: Twist, Lukas, et al.
Published: (2025)
by: Twist, Lukas, et al.
Published: (2025)
Early-Stage Prediction of Review Effort in AI-Generated Pull Requests
by: Minh, Dao Sy Duy, et al.
Published: (2026)
by: Minh, Dao Sy Duy, et al.
Published: (2026)
The Impact of Documentation on Test Engagement in Pull Requests in OSS
by: Amore, Teal, et al.
Published: (2026)
by: Amore, Teal, et al.
Published: (2026)
Empirical Analysis of Pull Requests for Google Summer of Code
by: Popoola, Saheed
Published: (2024)
by: Popoola, Saheed
Published: (2024)
GitHub Actions: The Impact on the Pull Request Process
by: Wessel, Mairieli, et al.
Published: (2022)
by: Wessel, Mairieli, et al.
Published: (2022)
Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance
by: Pinna, Giovanni, et al.
Published: (2026)
by: Pinna, Giovanni, 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)
by: Hao, Huizi, et al.
Published: (2024)
Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests
by: Gong, Jingzhi, et al.
Published: (2026)
by: Gong, Jingzhi, et al.
Published: (2026)
Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles
by: Jo, Young, et al.
Published: (2026)
by: Jo, Young, et al.
Published: (2026)
Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study
by: Peralta, Sien Reeve O., et al.
Published: (2026)
by: Peralta, Sien Reeve O., et al.
Published: (2026)
Social Bias in LLM-Generated Code: Benchmark and Mitigation
by: Rabbi, Fazle, et al.
Published: (2026)
by: Rabbi, Fazle, et al.
Published: (2026)
Analyzing GitHub Issues and Pull Requests in nf-core Pipelines: Insights into nf-core Pipeline Repositories
by: Alam, Khairul, et al.
Published: (2026)
by: Alam, Khairul, 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)
by: Watanabe, Miku, et al.
Published: (2025)
How Do Developers Use Code Suggestions in Pull Request Reviews?
by: Bouraffa, Abir, et al.
Published: (2025)
by: Bouraffa, Abir, et al.
Published: (2025)
Change And Cover: Last-Mile, Pull Request-Based Regression Test Augmentation
by: Zhou, Zitong, et al.
Published: (2026)
by: Zhou, Zitong, et al.
Published: (2026)
Similar Items
-
Insights into Security-Related AI-Generated Pull Requests
by: Rabbi, Md Fazle, et al.
Published: (2026) -
Insights into Dependency Maintenance Trends in the Maven Ecosystem
by: Chowdhury, Barisha, et al.
Published: (2025) -
Faster Releases, Fewer Risks: A Study on Maven Artifact Vulnerabilities and Lifecycle Management
by: Shafin, Md Shafiullah, et al.
Published: (2025) -
A Task-Level Evaluation of AI Agents in Open-Source Projects
by: Rahman, Shojibur, et al.
Published: (2026) -
When AI Teammates Meet Code Review: Collaboration Signals Shaping the Integration of Agent-Authored Pull Requests
by: Nachuma, Costain, et al.
Published: (2026)