Copertina

Wink: Recovering from Misbehaviors in Coding Agents

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Nanda, Rahul, Maddila, Chandra, Jha, Smriti, Khan, Euna Mehnaz, Paltenghi, Matteo, Chandra, Satish
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Software Engineering Artificial Intelligence Human-Computer Interaction Programming Languages
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage
di: Jha, Smriti, et al.
Pubblicazione: (2026)

Follow-up Attention: An Empirical Study of Developer and Neural Model Code Exploration
di: Paltenghi, Matteo, et al.
Pubblicazione: (2022)

Reading Between the Lines: Scalable User Feedback via Implicit Sentiment in Developer Prompts
di: Nam, Daye, et al.
Pubblicazione: (2025)

Agentic Code Reasoning
di: Ugare, Shubham, et al.
Pubblicazione: (2026)

When Web Apps Heal Themselves: A MAPE-K Based Approach to Fault Tolerance and Adaptive Recovery
di: Aribe Jr, Sales, et al.
Pubblicazione: (2026)

Understanding and supporting how developers prompt for LLM-powered code editing in practice
di: Nam, Daye, et al.
Pubblicazione: (2025)

Natural Language Outlines for Code: Literate Programming in the LLM Era
di: Shi, Kensen, et al.
Pubblicazione: (2024)

AI-Assisted Fixes to Code Review Comments at Scale
di: Maddila, Chandra, et al.
Pubblicazione: (2025)

Towards the Ultimate Programming Language: Trust and Benevolence in the Age of Artificial Intelligence
di: Sawicki, Bartosz, et al.
Pubblicazione: (2024)

Automated Visualization Code Synthesis via Multi-Path Reasoning and Feedback-Driven Optimization
di: Seo, Wonduk, et al.
Pubblicazione: (2025)

NeuroSync: Intent-Aware Code-Based Problem Solving via Direct LLM Understanding Modification
di: Zhang, Wenshuo, et al.
Pubblicazione: (2025)

Exploring the Potential of Conversational AI Support for Agent-Based Social Simulation Model Design
di: Siebers, Peer-Olaf
Pubblicazione: (2024)

Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025
di: Huang, Ruanqianqian, et al.
Pubblicazione: (2025)

How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions
di: Tang, Ningzhi, et al.
Pubblicazione: (2026)

Agentic Metacognition: Designing a "Self-Aware" Low-Code Agent for Failure Prediction and Human Handoff
di: Xu, Jiexi
Pubblicazione: (2025)

AIAP: A No-Code Workflow Builder for Non-Experts with Natural Language and Multi-Agent Collaboration
di: An, Hyunjn, et al.
Pubblicazione: (2025)

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

How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks
di: Bai, Longju, et al.
Pubblicazione: (2026)

CodeAlignBench: Assessing Code Generation Models on Developer-Preferred Code Adjustments
di: Mehralian, Forough, et al.
Pubblicazione: (2025)

Coding with Eyes: Visual Feedback Unlocks Reliable GUI Code Generating and Debugging
di: Liu, Zhilin, et al.
Pubblicazione: (2026)

Is Vibe Coding the Future? An Empirical Assessment of LLM Generated Codes for Construction Safety
di: Uddin, S M Jamil
Pubblicazione: (2026)

Accodemy: AI Powered Code Learning Platform to Assist Novice Programmers in Overcoming the Fear of Coding
di: Aamina, M. A. F., et al.
Pubblicazione: (2025)

Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototyping
di: Xiao, Jingyu, et al.
Pubblicazione: (2024)

Using an LLM to Help With Code Understanding
di: Nam, Daye, et al.
Pubblicazione: (2023)

More Code, Less Reuse: Investigating Code Quality and Reviewer Sentiment towards AI-generated Pull Requests
di: Huang, Haoming, et al.
Pubblicazione: (2026)

An Exploratory Study of ML Sketches and Visual Code Assistants
di: Gomes, Luís F., et al.
Pubblicazione: (2024)

Envisioning the Next-Generation AI Coding Assistants: Insights & Proposals
di: Nghiem, Khanh, et al.
Pubblicazione: (2024)

CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Coding
di: Luo, Hanjun, et al.
Pubblicazione: (2025)

CosmoCore Affective Dream-Replay Reinforcement Learning for Code Generation
di: Ravindran, Santhosh Kumar
Pubblicazione: (2025)

EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
di: Zhang, Yifan, et al.
Pubblicazione: (2024)

Can Developers Prompt? A Controlled Experiment for Code Documentation Generation
di: Kruse, Hans-Alexander, et al.
Pubblicazione: (2024)

"My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding Assistants
di: Lyu, Yunbo, et al.
Pubblicazione: (2025)

LABIIUM: AI-Enhanced Zero-configuration Measurement Automation System
di: Olowe, Emmanuel A., et al.
Pubblicazione: (2024)

ComboBench: Can LLMs Manipulate Physical Devices to Play Virtual Reality Games?
di: Li, Shuqing, et al.
Pubblicazione: (2025)

LLARS: Enabling Domain Expert & Developer Collaboration for LLM Prompting, Generation and Evaluation
di: Steigerwald, Philipp, et al.
Pubblicazione: (2026)

Using Large Language Models to Enhance Programming Error Messages
di: Leinonen, Juho, et al.
Pubblicazione: (2022)

A Phenomenological Approach to Analyzing User Queries in IT Systems Using Heidegger's Fundamental Ontology
di: Vishnevskiy, Maksim
Pubblicazione: (2025)

From Signal to Turn: Interactional Friction in Modular Speech-to-Speech Pipelines
di: Mairittha, Tittaya, et al.
Pubblicazione: (2025)

Comparative Analysis of Large Language Models for the Machine-Assisted Resolution of User Intentions
di: Flerlage, Justus, et al.
Pubblicazione: (2025)

Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs
di: Jin, Claire, et al.
Pubblicazione: (2024)