Copertina

Vibe Coding: Toward an AI-Native Paradigm for Semantic and Intent-Driven Programming

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Bamil, Vinay
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Software Engineering Human-Computer Interaction
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

(R)evolution of Programming: Vibe Coding as a Post-Coding Paradigm
di: Krings, Kevin, et al.
Pubblicazione: (2025)

Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agenda
di: Meske, Christian, et al.
Pubblicazione: (2025)

Computer Science Achievement and Writing Skills Predict Vibe Coding Proficiency
di: Thorgeirsson, Sverrir, et al.
Pubblicazione: (2026)

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

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

Understanding User Mental Models in AI-Driven Code Completion Tools: Insights from an Elicitation Study
di: Desolda, Giuseppe, et al.
Pubblicazione: (2025)

From OSS to Open Source AI: an Exploratory Study of Collaborative Development Paradigm Divergence
di: Ye, Hengzhi, et al.
Pubblicazione: (2026)

CodeA11y: Making AI Coding Assistants Useful for Accessible Web Development
di: Mowar, Peya, et al.
Pubblicazione: (2025)

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

GeoPandas-AI: A Smart Class Bringing LLM as Stateful AI Code Assistant
di: Merten, Gaspard, et al.
Pubblicazione: (2025)

Investigating and Designing for Trust in AI-powered Code Generation Tools
di: Wang, Ruotong, et al.
Pubblicazione: (2023)

Towards an Understanding of Developer Experience-Driven Transparency in Software Ecosystems
di: Zacarias, Rodrigo Oliveira, et al.
Pubblicazione: (2025)

Towards Decoding Developer Cognition in the Age of AI Assistants
di: Haque, Ebtesam Al, et al.
Pubblicazione: (2025)

Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise
di: Weisz, Justin D., et al.
Pubblicazione: (2024)

From Teacher to Colleague: How Coding Experience Shapes Developer Perceptions of AI Tools
di: Zakharov, Ilya, et al.
Pubblicazione: (2025)

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

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

GazeCopilot: Evaluating Novel Gaze-Informed Prompting for AI-Supported Code Comprehension and Readability
di: Elfares, Yasmine, et al.
Pubblicazione: (2025)

RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reporting
di: Rao, Pooja S. B., et al.
Pubblicazione: (2025)

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

Building Software by Rolling the Dice: A Qualitative Study of Vibe Coding
di: Chou, Yi-Hung, et al.
Pubblicazione: (2025)

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

Explaining Code Examples in Introductory Programming Courses: LLM vs Humans
di: Lekshmi-Narayanan, Arun-Balajiee, et al.
Pubblicazione: (2023)

"I Would Have Written My Code Differently'': Beginners Struggle to Understand LLM-Generated Code
di: Zi, Yangtian, et al.
Pubblicazione: (2025)

Gendered Prompting and LLM Code Review: How Gender Cues in the Prompt Shape Code Quality and Evaluation
di: Janzen, Lynn, et al.
Pubblicazione: (2026)

Automatic Bias Detection in Source Code Review
di: Alebachew, Yoseph Berhanu, et al.
Pubblicazione: (2025)

Assessing Consensus of Developers' Views on Code Readability
di: Sergeyuk, Agnia, et al.
Pubblicazione: (2024)

How Scientists Use Large Language Models to Program
di: O'Brien, Gabrielle
Pubblicazione: (2025)

A Systematic Mapping Study on Chatbots in Programming Education
di: Garcia, Marcelino, et al.
Pubblicazione: (2025)

Anteater: Interactive Visualization of Program Execution Values in Context
di: Faust, Rebecca, et al.
Pubblicazione: (2019)

Generating Complex Code Analyzers from Natural Language Questions
di: Nazari, Amirmohammad, et al.
Pubblicazione: (2026)

Detecting UX smells in Visual Studio Code using LLMs
di: Rodriguez, Andrés, et al.
Pubblicazione: (2026)

Code Compass: A Study on the Challenges of Navigating Unfamiliar Codebases
di: Agrawal, Ekansh, et al.
Pubblicazione: (2024)

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

A Low-Code Approach for the Automatic Personalization of Conversational Agents
di: Conrardy, Aaron, et al.
Pubblicazione: (2026)

Codetations: Intelligent, Persistent Notes and UIs for Programs and Other Documents
di: Misback, Edward, et al.
Pubblicazione: (2025)

SPARK: Real-Time Monitoring of Multi-Faceted Programming Exercises
di: Yang, Yinuo, et al.
Pubblicazione: (2026)

Reassessing Java Code Readability Models with a Human-Centered Approach
di: Sergeyuk, Agnia, et al.
Pubblicazione: (2024)

MLLM-Based UI2Code Automation Guided by UI Layout Information
di: Wu, Fan, et al.
Pubblicazione: (2025)

Exploring Direct Instruction and Summary-Mediated Prompting in LLM-Assisted Code Modification
di: Tang, Ningzhi, et al.
Pubblicazione: (2025)