Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software
Fuente:
arXiv
Saved in:
| Main Authors: | KC, Pragyan, Ghandiparsi, Rambod, Herron, Thomas, Heaps, John, Hosseini, Mitra Bokaei |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Towards Better Requirements from the Crowd: Developer Engagement with Feature Requests in Open Source Software
by: KC, Pragyan, et al.
Published: (2025)
by: KC, Pragyan, et al.
Published: (2025)
Toward Regulatory Compliance: A few-shot Learning Approach to Extract Processing Activities
by: KC, Pragyan, et al.
Published: (2024)
by: KC, Pragyan, et al.
Published: (2024)
An Analysis of Automated Use Case Component Extraction from Scenarios using ChatGPT
by: KC, Pragyan, et al.
Published: (2024)
by: KC, Pragyan, et al.
Published: (2024)
Feature Request Analysis and Processing: Tasks, Techniques, and Trends
by: Niu, Feifei, et al.
Published: (2025)
by: Niu, Feifei, et al.
Published: (2025)
The Death Spiral of Open Source Projects: A Post-Mortem Analysis of Pull Request Workflow Dynamics
by: Kaushik, Mohit, et al.
Published: (2026)
by: Kaushik, Mohit, 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)
Predicting Abandonment of Open Source Software Projects with An Integrated Feature Framework
by: Xu, Yiming, et al.
Published: (2025)
by: Xu, Yiming, 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)
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)
Insights into Security-Related AI-Generated Pull Requests
by: Rabbi, Md Fazle, et al.
Published: (2026)
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)
by: Amore, Teal, 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)
Adaptive Request Scheduling for CodeLLM Serving with SLA Guarantees
by: Chang, Shi, et al.
Published: (2025)
by: Chang, Shi, et al.
Published: (2025)
An Empirical Study on the Amount of Changes Required for Merge Request Acceptance
by: Kansab, Samah, et al.
Published: (2025)
by: Kansab, Samah, 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)
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)
The Quiet Contributions: Insights into AI-Generated Silent Pull Requests
by: Hasan, S M Mahedy, et al.
Published: (2026)
by: Hasan, S M Mahedy, 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)
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)
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)
Improving Merge Pipeline Throughput in Continuous Integration via Pull Request Prioritization
by: Jungwirth, Maximilian, et al.
Published: (2025)
by: Jungwirth, Maximilian, et al.
Published: (2025)
Group versus Individual Review Requests: Tradeoffs in Speed and Quality at Mozilla Firefox
by: Kucera, Matej, et al.
Published: (2026)
by: Kucera, Matej, et al.
Published: (2026)
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)
Analyzing DevOps Practices Through Merge Request Data: A Case Study in Networking Software Company
by: Kansab, Samah, et al.
Published: (2025)
by: Kansab, Samah, et al.
Published: (2025)
What Developers Ask to ChatGPT in GitHub Pull Requests? an Exploratory Study
by: Silva, Julyanara R., et al.
Published: (2025)
by: Silva, Julyanara R., et al.
Published: (2025)
On the Footprints of Reviewer Bots Feedback on Agentic Pull Requests in OSS GitHub Repositories
by: Fatima, Syeda Kaneez, et al.
Published: (2026)
by: Fatima, Syeda Kaneez, et al.
Published: (2026)
Mint: Cost-Efficient Tracing with All Requests Collection via Commonality and Variability Analysis
by: Huang, Haiyu, et al.
Published: (2024)
by: Huang, Haiyu, et al.
Published: (2024)
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)
Humans Integrate, Agents Fix: How Agent-Authored Pull Requests Are Referenced in Practice
by: Khemissi, Islem, et al.
Published: (2026)
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)
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)
by: Wessel, Mairieli, et al.
Published: (2021)
Sphinx: Benchmarking and Modeling for LLM-Driven Pull Request Review
by: Zhang, Daoan, et al.
Published: (2026)
by: Zhang, Daoan, et al.
Published: (2026)
MELT: Mining Effective Lightweight Transformations from Pull Requests
by: Ramos, Daniel, et al.
Published: (2023)
by: Ramos, Daniel, et al.
Published: (2023)
LLM-Redactor: An Empirical Evaluation of Eight Techniques for Privacy-Preserving LLM Requests
by: Agyemang, Justice Owusu, et al.
Published: (2026)
by: Agyemang, Justice Owusu, et al.
Published: (2026)
A Mixed-Methods Study of Open-Source Software Maintainers On Vulnerability Management and Platform Security Features
by: Ayala, Jessy, et al.
Published: (2024)
by: Ayala, Jessy, et al.
Published: (2024)
Demystifying Issues, Causes and Solutions in LLM Open-Source Projects
by: Cai, Yangxiao, et al.
Published: (2024)
by: Cai, Yangxiao, et al.
Published: (2024)
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)
From Industry Claims to Empirical Reality: An Empirical Study of Code Review Agents in Pull Requests
by: Chowdhury, Kowshik, et al.
Published: (2026)
by: Chowdhury, Kowshik, et al.
Published: (2026)
Similar Items
-
Towards Better Requirements from the Crowd: Developer Engagement with Feature Requests in Open Source Software
by: KC, Pragyan, et al.
Published: (2025) -
Toward Regulatory Compliance: A few-shot Learning Approach to Extract Processing Activities
by: KC, Pragyan, et al.
Published: (2024) -
An Analysis of Automated Use Case Component Extraction from Scenarios using ChatGPT
by: KC, Pragyan, et al.
Published: (2024) -
Feature Request Analysis and Processing: Tasks, Techniques, and Trends
by: Niu, Feifei, et al.
Published: (2025) -
The Death Spiral of Open Source Projects: A Post-Mortem Analysis of Pull Request Workflow Dynamics
by: Kaushik, Mohit, et al.
Published: (2026)