Cover Image

When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mozannar, Hussein, Bansal, Gagan, Fourney, Adam, Horvitz, Eric
Format:	Preprint
Published:	2023
Subjects:	Human-Computer Interaction Machine Learning Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming
by: Mozannar, Hussein, et al.
Published: (2022)

Navigating Rifts in Human-LLM Grounding: Study and Benchmark
by: Shaikh, Omar, et al.
Published: (2025)

AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems
by: Dibia, Victor, et al.
Published: (2024)

Challenges in Human-Agent Communication
by: Bansal, Gagan, et al.
Published: (2024)

Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?
by: Kou, Bonan, et al.
Published: (2023)

Concept-Guided LLM Agents for Human-AI Safety Codesign
by: Geissler, Florian, et al.
Published: (2024)

Scaling Human-AI Coding Collaboration Requires a Governable Consensus Layer
by: Wang, Tianfu, et al.
Published: (2026)

Human-in-the-Loop Synthetic Text Data Inspection with Provenance Tracking
by: Kang, Hong Jin, et al.
Published: (2024)

Integrating Human Feedback into a Reinforcement Learning-Based Framework for Adaptive User Interfaces
by: Gaspar-Figueiredo, Daniel, et al.
Published: (2025)

The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers
by: Mozannar, Hussein, et al.
Published: (2024)

Pre-Filtering Code Suggestions using Developer Behavioral Telemetry to Optimize LLM-Assisted Programming
by: Awad, Mohammad Nour Al, et al.
Published: (2025)

SHAPR: A Solo Human-Centred and AI-Assisted Practice Framework for Research Software Development
by: Chan, Ka Ching
Published: (2026)

Show Me Why It's Correct: Saving 1/3 of Debugging Time in Program Repair with Interactive Runtime Comparison
by: Wang, Ruixin, et al.
Published: (2025)

The Impact of Prompt Programming on Function-Level Code Generation
by: Khojah, Ranim, et al.
Published: (2024)

EcoAssist: Embedding Sustainability into AI-Assisted Frontend Development
by: Barrocas, André, et al.
Published: (2026)

Programming by Chat: A Large-Scale Behavioral Analysis of 11,579 Real-World AI-Assisted IDE Sessions
by: Tang, Ningzhi, et al.
Published: (2026)

EM-Assist: Safe Automated ExtractMethod Refactoring with LLMs
by: Pomian, Dorin, et al.
Published: (2024)

Optimizing LLM Code Suggestions: Feedback-Driven Timing with Lightweight State Bounds
by: Awad, Mohammad Nour Al, et al.
Published: (2025)

Patterns of Developer Adoption of LLM-Generated Code Refactoring Suggestions
by: Schön, David, et al.
Published: (2026)

Age Matters: Analyzing Age-Related Discussions in App Reviews
by: Nirmania, Shashiwadana, et al.
Published: (2026)

Smart Paste: Automatically Fixing Copy/Paste for Google Developers
by: Nguyen, Vincent, et al.
Published: (2025)

Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settings
by: Tsvetkov, Petr, et al.
Published: (2024)

Can LLMs Replace Manual Annotation of Software Engineering Artifacts?
by: Ahmed, Toufique, et al.
Published: (2024)

Enhancing Code LLM Training with Programmer Attention
by: Zhang, Yifan, et al.
Published: (2025)

Human-In-the-Loop Software Development Agents
by: Takerngsaksiri, Wannita, et al.
Published: (2024)

RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions
by: He, Keyu, et al.
Published: (2026)

Evolution of Programmers' Trust in Generative AI Programming Assistants
by: Shah, Anshul, et al.
Published: (2025)

Bug Detective and Quality Coach: Developers' Mental Models of AI-Assisted IDE Tools
by: Buono, Paolo, et al.
Published: (2025)

The Impact of AI-Assisted Development on Software Security: A Study of Gemini and Developer Experience
by: Jost, Nadine, et al.
Published: (2026)

Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks
by: Etsenake, Deborah, et al.
Published: (2024)

Vibe Coding: Toward an AI-Native Paradigm for Semantic and Intent-Driven Programming
by: Bamil, Vinay
Published: (2025)

Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph
by: Exarhakos, Vassilios, et al.
Published: (2026)

On the Automated Processing of User Feedback
by: Maalej, Walid, et al.
Published: (2024)

How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging
by: Ma, Qianou, et al.
Published: (2023)

How Do Developers Interact with AI? An Exploratory Study on Modeling Developer Programming Behavior
by: Wu, Yinan, et al.
Published: (2026)

Navigating Compiler Errors with AI Assistance -- A Study of GPT Hints in an Introductory Programming Course
by: Pankiewicz, Maciej, et al.
Published: (2024)

Single Conversation Methodology: A Human-Centered Protocol for AI-Assisted Software Development
by: Escobedo, Salvador D.
Published: (2025)

In-IDE Human-AI Experience in the Era of Large Language Models; A Literature Review
by: Sergeyuk, Agnia, et al.
Published: (2024)

Bridging the Socio-Emotional Gap: The Functional Dimension of Human-AI Collaboration for Software Engineering
by: Rani, Lekshmi Murali, et al.
Published: (2026)

Evolving with AI: A Longitudinal Analysis of Developer Logs
by: Sergeyuk, Agnia, et al.
Published: (2026)