Patterns of Developer Adoption of LLM-Generated Code Refactoring Suggestions
Fuente:
arXiv
Saved in:
| Main Authors: | Schön, David, Amjad, Faiza, Asif, Tehreem, Khojah, Ranim, Mohamad, Mazen, Neto, Francisco Gomes de Oliveira, Leitner, Philipp |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The Impact of Prompt Programming on Function-Level Code Generation
by: Khojah, Ranim, et al.
Published: (2024)
by: Khojah, Ranim, et al.
Published: (2024)
Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice
by: Khojah, Ranim, et al.
Published: (2024)
by: Khojah, Ranim, et al.
Published: (2024)
From Human-to-Human to Human-to-Bot Conversations in Software Engineering
by: Khojah, Ranim, et al.
Published: (2024)
by: Khojah, Ranim, et al.
Published: (2024)
LLM Company Policies and Policy Implications in Software Organizations
by: Khojah, Ranim, et al.
Published: (2025)
by: Khojah, Ranim, et al.
Published: (2025)
Trust Calibration in IDEs: Paving the Way for Widespread Adoption of AI Refactoring
by: Borg, Markus
Published: (2024)
by: Borg, Markus
Published: (2024)
Pre-Filtering Code Suggestions using Developer Behavioral Telemetry to Optimize LLM-Assisted Programming
by: Awad, Mohammad Nour Al, et al.
Published: (2025)
by: Awad, Mohammad Nour Al, et al.
Published: (2025)
Optimizing LLM Code Suggestions: Feedback-Driven Timing with Lightweight State Bounds
by: Awad, Mohammad Nour Al, et al.
Published: (2025)
by: Awad, Mohammad Nour Al, et al.
Published: (2025)
From Gains to Strains: Modeling Developer Burnout with GenAI Adoption
by: Feng, Zixuan, et al.
Published: (2025)
by: Feng, Zixuan, et al.
Published: (2025)
What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI
by: Choudhuri, Rudrajit, et al.
Published: (2025)
by: Choudhuri, Rudrajit, et al.
Published: (2025)
Exit the Code: A Model for Understanding Career Abandonment Intention Among Software Developers
by: Massoni, Tiago, et al.
Published: (2025)
by: Massoni, Tiago, et al.
Published: (2025)
Emotional Strain and Frustration in LLM Interactions in Software Engineering
by: Montes, Cristina Martinez, et al.
Published: (2025)
by: Montes, Cristina Martinez, et al.
Published: (2025)
A Study on Developer Behaviors for Validating and Repairing LLM-Generated Code Using Eye Tracking and IDE Actions
by: Tang, Ningzhi, et al.
Published: (2024)
by: Tang, Ningzhi, et al.
Published: (2024)
Teaching Agile Requirements Engineering: A Stakeholder Simulation with Generative AI
by: Schön, Eva-Maria, et al.
Published: (2026)
by: Schön, Eva-Maria, et al.
Published: (2026)
Assessing Consensus of Developers' Views on Code Readability
by: Sergeyuk, Agnia, et al.
Published: (2024)
by: Sergeyuk, Agnia, et al.
Published: (2024)
CodeA11y: Making AI Coding Assistants Useful for Accessible Web Development
by: Mowar, Peya, et al.
Published: (2025)
by: Mowar, Peya, et al.
Published: (2025)
"I Would Have Written My Code Differently'': Beginners Struggle to Understand LLM-Generated Code
by: Zi, Yangtian, et al.
Published: (2025)
by: Zi, Yangtian, et al.
Published: (2025)
Gendered Prompting and LLM Code Review: How Gender Cues in the Prompt Shape Code Quality and Evaluation
by: Janzen, Lynn, et al.
Published: (2026)
by: Janzen, Lynn, et al.
Published: (2026)
AI in Software Engineering: Perceived Roles and Their Impact on Adoption
by: Zakharov, Ilya, et al.
Published: (2025)
by: Zakharov, Ilya, et al.
Published: (2025)
Developer Interaction Patterns with Proactive AI: A Five-Day Field Study
by: Kuo, Nadine, et al.
Published: (2026)
by: Kuo, Nadine, et al.
Published: (2026)
Cognitive Biases in LLM-Assisted Software Development
by: Zhou, Xinyi, et al.
Published: (2026)
by: Zhou, Xinyi, et al.
Published: (2026)
Exploring Direct Instruction and Summary-Mediated Prompting in LLM-Assisted Code Modification
by: Tang, Ningzhi, et al.
Published: (2025)
by: Tang, Ningzhi, et al.
Published: (2025)
Democratizing AI Development: Local LLM Deployment for India's Developer Ecosystem in the Era of Tokenized APIs
by: Udandarao, Vikranth, et al.
Published: (2025)
by: Udandarao, Vikranth, et al.
Published: (2025)
Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise
by: Weisz, Justin D., et al.
Published: (2024)
by: Weisz, Justin D., et al.
Published: (2024)
Emotional Contagion in Code: How GitHub Emoji Reactions Shape Developer Collaboration
by: Kraishan, Obada
Published: (2025)
by: Kraishan, Obada
Published: (2025)
From Teacher to Colleague: How Coding Experience Shapes Developer Perceptions of AI Tools
by: Zakharov, Ilya, et al.
Published: (2025)
by: Zakharov, Ilya, et al.
Published: (2025)
GeoPandas-AI: A Smart Class Bringing LLM as Stateful AI Code Assistant
by: Merten, Gaspard, et al.
Published: (2025)
by: Merten, Gaspard, et al.
Published: (2025)
Exploring Developer Experience Factors in Software Ecosystems
by: Zacarias, Rodrigo Oliveira, et al.
Published: (2025)
by: Zacarias, Rodrigo Oliveira, et al.
Published: (2025)
Empowering Software Engineers to Design More Secure Web Applications: Guidelines and Potential of Using LLMs as a Recommender Tool
by: Raffaela Groner, et al.
Published: (2026)
by: Raffaela Groner, et al.
Published: (2026)
Towards an Understanding of Developer Experience-Driven Transparency in Software Ecosystems
by: Zacarias, Rodrigo Oliveira, et al.
Published: (2025)
by: Zacarias, Rodrigo Oliveira, et al.
Published: (2025)
Using an LLM to Help With Code Understanding
by: Nam, Daye, et al.
Published: (2023)
by: Nam, Daye, et al.
Published: (2023)
CodeAlignBench: Assessing Code Generation Models on Developer-Preferred Code Adjustments
by: Mehralian, Forough, et al.
Published: (2025)
by: Mehralian, Forough, et al.
Published: (2025)
EM-Assist: Safe Automated ExtractMethod Refactoring with LLMs
by: Pomian, Dorin, et al.
Published: (2024)
by: Pomian, Dorin, et al.
Published: (2024)
Is Vibe Coding the Future? An Empirical Assessment of LLM Generated Codes for Construction Safety
by: Uddin, S M Jamil
Published: (2026)
by: Uddin, S M Jamil
Published: (2026)
Enhancing Code LLM Training with Programmer Attention
by: Zhang, Yifan, et al.
Published: (2025)
by: Zhang, Yifan, et al.
Published: (2025)
LikeThis! Empowering App Users to Submit UI Improvement Suggestions Instead of Complaints
by: Wei, Jialiang, et al.
Published: (2026)
by: Wei, Jialiang, et al.
Published: (2026)
When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming
by: Mozannar, Hussein, et al.
Published: (2023)
by: Mozannar, Hussein, et al.
Published: (2023)
"Always Nice and Confident, Sometimes Wrong": Developer's Experiences Engaging Large Language Models (LLMs) Versus Human-Powered Q&A Platforms for Coding Support
by: Li, Jiachen, et al.
Published: (2023)
by: Li, Jiachen, et al.
Published: (2023)
Explaining Code with a Purpose: An Integrated Approach for Developing Code Comprehension and Prompting Skills
by: Denny, Paul, et al.
Published: (2024)
by: Denny, Paul, et al.
Published: (2024)
Investigating Multimodal Large Language Models to Support Usability Evaluation
by: Lubos, Sebastian, et al.
Published: (2025)
by: Lubos, Sebastian, et al.
Published: (2025)
An Exploratory Study of ML Sketches and Visual Code Assistants
by: Gomes, Luís F., et al.
Published: (2024)
by: Gomes, Luís F., et al.
Published: (2024)
Similar Items
-
The Impact of Prompt Programming on Function-Level Code Generation
by: Khojah, Ranim, et al.
Published: (2024) -
Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice
by: Khojah, Ranim, et al.
Published: (2024) -
From Human-to-Human to Human-to-Bot Conversations in Software Engineering
by: Khojah, Ranim, et al.
Published: (2024) -
LLM Company Policies and Policy Implications in Software Organizations
by: Khojah, Ranim, et al.
Published: (2025) -
Trust Calibration in IDEs: Paving the Way for Widespread Adoption of AI Refactoring
by: Borg, Markus
Published: (2024)