Guardado en:
| Autores principales: | Agyemang, Justice Owusu, Kponyo, Jerry John, Amponsah, Elliot, Boakye, Godfred Manu Addo, Agyekum, Kwame Opuni-Boachie Obour |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2604.12064 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Resilient Write: A Six-Layer Durable Write Surface for LLM Coding Agents
por: Agyemang, Justice Owusu, et al.
Publicado: (2026)
por: Agyemang, Justice Owusu, et al.
Publicado: (2026)
Local-Splitter: A Measurement Study of Seven Tactics for Reducing Cloud LLM Token Usage on Coding-Agent Workloads
por: Agyemang, Justice Owusu, et al.
Publicado: (2026)
por: Agyemang, Justice Owusu, et al.
Publicado: (2026)
HiveMind: OS-Inspired Scheduling for Concurrent LLM Agent Workloads
por: Agyemang, Justice Owusu, et al.
Publicado: (2026)
por: Agyemang, Justice Owusu, et al.
Publicado: (2026)
The Streaming Reservoir Convergence Theorem: A Prospect-Theoretic Framework for Multi-Provider Adaptive Streaming
por: Agyemang, Justice Owusu, et al.
Publicado: (2026)
por: Agyemang, Justice Owusu, et al.
Publicado: (2026)
Robustness Analysis of Machine Learning Models for IoT Intrusion Detection Under Data Poisoning Attacks
por: Wulnye, Fortunatus Aabangbio, et al.
Publicado: (2026)
por: Wulnye, Fortunatus Aabangbio, et al.
Publicado: (2026)
When Agents Go Quiet: Output Generation Capacity and Format-Cost Separation for LLM Document Synthesis
por: Agyemang, Justice Owusu, et al.
Publicado: (2026)
por: Agyemang, Justice Owusu, et al.
Publicado: (2026)
A transformer-based deep q learning approach for dynamic load balancing in software-defined networks
por: Owusu, Evans Tetteh, et al.
Publicado: (2025)
por: Owusu, Evans Tetteh, et al.
Publicado: (2025)
Adaptive Request Scheduling for CodeLLM Serving with SLA Guarantees
por: Chang, Shi, et al.
Publicado: (2025)
por: Chang, Shi, et al.
Publicado: (2025)
Porting an LLM based Application from ChatGPT to an On-Premise Environment
por: Paloniemi, Teemu, et al.
Publicado: (2025)
por: Paloniemi, Teemu, et al.
Publicado: (2025)
Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests
por: Yoshioka, Haruhiko, et al.
Publicado: (2026)
por: Yoshioka, Haruhiko, et al.
Publicado: (2026)
Empirical Analysis of Pull Requests for Google Summer of Code
por: Popoola, Saheed
Publicado: (2024)
por: Popoola, Saheed
Publicado: (2024)
Sphinx: Benchmarking and Modeling for LLM-Driven Pull Request Review
por: Zhang, Daoan, et al.
Publicado: (2026)
por: Zhang, Daoan, et al.
Publicado: (2026)
Feature Request Analysis and Processing: Tasks, Techniques, and Trends
por: Niu, Feifei, et al.
Publicado: (2025)
por: Niu, Feifei, et al.
Publicado: (2025)
From Queries to Insights: Agentic LLM Pipelines for Spatio-Temporal Text-to-SQL
por: Redd, Manu, et al.
Publicado: (2025)
por: Redd, Manu, et al.
Publicado: (2025)
Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study
por: Peralta, Sien Reeve O., et al.
Publicado: (2026)
por: Peralta, Sien Reeve O., et al.
Publicado: (2026)
An Empirical Study on the Amount of Changes Required for Merge Request Acceptance
por: Kansab, Samah, et al.
Publicado: (2025)
por: Kansab, Samah, et al.
Publicado: (2025)
From Industry Claims to Empirical Reality: An Empirical Study of Code Review Agents in Pull Requests
por: Chowdhury, Kowshik, et al.
Publicado: (2026)
por: Chowdhury, Kowshik, et al.
Publicado: (2026)
LLM-Based Repair of Static Nullability Errors
por: Karimipour, Nima, et al.
Publicado: (2025)
por: Karimipour, Nima, et al.
Publicado: (2025)
On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub
por: Watanabe, Miku, et al.
Publicado: (2025)
por: Watanabe, Miku, et al.
Publicado: (2025)
AutoFeedback: An LLM-based Framework for Efficient and Accurate API Request Generation
por: Liu, Huanxi, et al.
Publicado: (2024)
por: Liu, Huanxi, et al.
Publicado: (2024)
When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair
por: Luo, Wenqiang, et al.
Publicado: (2024)
por: Luo, Wenqiang, et al.
Publicado: (2024)
An Empirical Study on Challenges for LLM Application Developers
por: Chen, Xiang, et al.
Publicado: (2024)
por: Chen, Xiang, et al.
Publicado: (2024)
Green LLM Techniques in Action: How Effective Are Existing Techniques for Improving the Energy Efficiency of LLM-Based Applications in Industry?
por: Kuran, Pelin Rabia, et al.
Publicado: (2026)
por: Kuran, Pelin Rabia, et al.
Publicado: (2026)
You Can REST Now: Automated REST API Documentation and Testing via LLM-Assisted Request Mutations
por: Decrop, Alix, et al.
Publicado: (2024)
por: Decrop, Alix, et al.
Publicado: (2024)
An Empirical Study of LLM-Based Code Clone Detection
por: Zhu, Wenqing, et al.
Publicado: (2025)
por: Zhu, Wenqing, et al.
Publicado: (2025)
An Empirical Study of Bugs in Modern LLM Agent Frameworks
por: Zhu, Xinxue, et al.
Publicado: (2026)
por: Zhu, Xinxue, et al.
Publicado: (2026)
An Empirical Study on Developers Shared Conversations with ChatGPT in GitHub Pull Requests and Issues
por: Hao, Huizi, et al.
Publicado: (2024)
por: Hao, Huizi, et al.
Publicado: (2024)
Rethinking Code Review Workflows with LLM Assistance: An Empirical Study
por: Aðalsteinsson, Fannar Steinn, et al.
Publicado: (2025)
por: Aðalsteinsson, Fannar Steinn, et al.
Publicado: (2025)
LLM-based Vulnerability Detection at Project Scale: An Empirical Study
por: Li, Fengjie, et al.
Publicado: (2026)
por: Li, Fengjie, et al.
Publicado: (2026)
Supporting Stakeholder Requirements Expression with LLM Revisions: An Empirical Evaluation
por: Mircea, Michael, et al.
Publicado: (2026)
por: Mircea, Michael, et al.
Publicado: (2026)
AutoEmpirical: LLM-Based Automated Research for Empirical Software Fault Analysis
por: Yu, Jiongchi, et al.
Publicado: (2025)
por: Yu, Jiongchi, et al.
Publicado: (2025)
Environment-in-the-Loop: Rethinking Code Migration with LLM-based Agents
por: Li, Xiang, et al.
Publicado: (2026)
por: Li, Xiang, et al.
Publicado: (2026)
Security in the Age of AI Teammates: An Empirical Study of Agentic Pull Requests on GitHub
por: Siddiq, Mohammed Latif, et al.
Publicado: (2026)
por: Siddiq, Mohammed Latif, et al.
Publicado: (2026)
Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis
por: Mu, Yanzhou, et al.
Publicado: (2025)
por: Mu, Yanzhou, et al.
Publicado: (2025)
Revisiting Vulnerability Patch Localization: An Empirical Study and LLM-Based Solution
por: Xu, Haoran, et al.
Publicado: (2025)
por: Xu, Haoran, et al.
Publicado: (2025)
An Empirical Study of Vulnerable Package Dependencies in LLM Repositories
por: Liu, Shuhan, et al.
Publicado: (2025)
por: Liu, Shuhan, et al.
Publicado: (2025)
Beyond Bug Fixes: An Empirical Investigation of Post-Merge Code Quality Issues in Agent-Generated Pull Requests
por: Cynthia, Shamse Tasnim, et al.
Publicado: (2026)
por: Cynthia, Shamse Tasnim, et al.
Publicado: (2026)
Methodological Evaluation and Risk Reduction Modelling of Public Health Surveillance Systems in Ghana: A Bayesian Hierarchical Meta-Analysis, 2000–2026
por: Agyemang, Kwame
Publicado: (2011)
por: Agyemang, Kwame
Publicado: (2011)
Why Are AI Agent Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study
por: Alam, Khairul, et al.
Publicado: (2026)
por: Alam, Khairul, et al.
Publicado: (2026)
LLM-Assisted Empirical Software Engineering: Systematic Literature Review and Research Agenda
por: Gomes, Victoria, et al.
Publicado: (2026)
por: Gomes, Victoria, et al.
Publicado: (2026)
Ejemplares similares
-
Resilient Write: A Six-Layer Durable Write Surface for LLM Coding Agents
por: Agyemang, Justice Owusu, et al.
Publicado: (2026) -
Local-Splitter: A Measurement Study of Seven Tactics for Reducing Cloud LLM Token Usage on Coding-Agent Workloads
por: Agyemang, Justice Owusu, et al.
Publicado: (2026) -
HiveMind: OS-Inspired Scheduling for Concurrent LLM Agent Workloads
por: Agyemang, Justice Owusu, et al.
Publicado: (2026) -
The Streaming Reservoir Convergence Theorem: A Prospect-Theoretic Framework for Multi-Provider Adaptive Streaming
por: Agyemang, Justice Owusu, et al.
Publicado: (2026) -
Robustness Analysis of Machine Learning Models for IoT Intrusion Detection Under Data Poisoning Attacks
por: Wulnye, Fortunatus Aabangbio, et al.
Publicado: (2026)