# Vibe coding: programming through conversation with artificial intelligence

arXiv: https://arxiv.org/abs/2506.23253 · HTML: https://mlsanity.com/paper/2506.23253/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2025.

## Built on by (most cited first, 29 of 29)

- [Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks](https://mlsanity.com/paper/2512.03262/) (2025)
- [Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025](https://mlsanity.com/paper/2512.14012/) (2025)
- [Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agenda](https://mlsanity.com/paper/2507.21928/) (2025)
- [LOGOS: LLM-driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Research](https://mlsanity.com/paper/2509.24294/) (2025)
- [Exploring the Challenges and Opportunities of AI-assisted Codebase Generation](https://mlsanity.com/paper/2508.07966/) (2025)
- [Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaboration](https://mlsanity.com/paper/2509.10652/) (2025)
- [DuetUI: A Bidirectional Context Loop for Human-Agent Co-Generation of Task-Oriented Interfaces](https://mlsanity.com/paper/2509.13444/) (2025)
- [SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering](https://mlsanity.com/paper/2605.17526/) (2026)
- [Architecture Without Architects: How AI Coding Agents Shape Software Architecture](https://mlsanity.com/paper/2604.04990/) (2026)
- [Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering](https://mlsanity.com/paper/2607.01087/) (2026)
- [Alignment-Process-Outcome: Rethinking How AIs and Humans Collaborate](https://mlsanity.com/paper/2603.08017/) (2026)
- [Interrogating Design Homogenization in Web Vibe Coding](https://mlsanity.com/paper/2603.13036/) (2026)
- [Do AI Models Dream of Faster Code? An Empirical Study on LLM-Proposed Performance Improvements in Real-World Software](https://mlsanity.com/paper/2510.15494/) (2025)
- [Vibe Coding, Interface Flattening](https://mlsanity.com/paper/2512.24939/) (2025)
- [Context Before Code: An Experience Report on Vibe Coding in Practice](https://mlsanity.com/paper/2603.11073/) (2026)
- [Is Vibe Coding the Future? An Empirical Assessment of LLM Generated Codes for Construction Safety](https://mlsanity.com/paper/2604.12311/) (2026)
- [SCAN: A Decision-Making Framework for Effective Task Allocation with Generative AI](https://mlsanity.com/paper/2606.15601/) (2026)
- [Case study: solving P-99 with LPTP and an LLM](https://mlsanity.com/paper/2607.21196/) (2026)
- [Design Theater: Evaluating the Gap Between User-Facing Design Reasoning and Implementation in Generative UI Tools](https://mlsanity.com/paper/2607.22928/) (2026)
- [From Idea to Classroom in Days: Using "Vibe Coding" to Create a Programming Process Visualizer from IDE Activity Logs](https://mlsanity.com/paper/2607.24757/) (2026)
- [Vibe-FDTR: An agent-oriented framework for reproducible frequency-domain thermoreflectance data analysis](https://mlsanity.com/paper/2607.28200/) (2026)
- [Vibe Compiler: A Research-Logic Synthesis Tool That Runs without Prompt Engineering -Toward Enhancing Metacognition for Sustaining Agency in the Age of Generative AI-](https://mlsanity.com/paper/2608.05545/) (2026)
- [LangChoiceBench: Measuring and Explaining Programming-Language Choice in LLMs](https://mlsanity.com/paper/2608.06041/) (2026)
- [Accelerated Genetic Programming Hyper-Heuristics for Simulation-Based Scheduling via Agentic AI](https://mlsanity.com/paper/2608.19487/) (2026)
- [The Vibe Shift in Software Engineering: Evaluating AI-Led Conversational Programming for Performance, Cognition, and Responsible Adoption](https://mlsanity.com/paper/2609.09560/) (2026)
- [Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflows](https://mlsanity.com/paper/2609.26725/) (2026)
- [Vibe Analysis: Exploring LLM Adoption by Data Visualization Practitioners](https://mlsanity.com/paper/2609.31922/) (2026)
- [AuraForge: Scaling Security Supervision for Training Coding Agents](https://mlsanity.com/paper/2610.00850/) (2026)
- [Last Secure Code Benchmark: Is the Code You Vibe Secure?](https://mlsanity.com/paper/2610.14766/) (2026)

## Abstract (arXiv)

We examine "vibe coding": an emerging programming paradigm where developers primarily write code by interacting with code-generating large language models rather than writing code directly. We present the first empirical study of vibe coding. We analysed over 8 hours of curated video capturing extended vibe coding sessions with rich think-aloud reflections. Using framework analysis, we investigated programmers' goals, workflows, prompting techniques, debugging approaches, and challenges encountered. We find that vibe coding follows iterative goal satisfaction cycles where developers alternate between prompting AI, evaluating generated code through rapid scanning and application testing, and manual editing. Prompts in vibe coding blend vague, high-level directives with detailed technical specifications. Debugging remains a hybrid process combining AI assistance with manual practices. Critically, vibe coding does not eliminate the need for programming expertise but rather redistributes it toward context management, rapid code evaluation, and decisions about when to transition between AI-driven and manual manipulation of code. Trust in AI tools during vibe coding is dynamic and contextual, developed through iterative verification rather than blanket acceptance. Vibe coding is an evolution of AI-assisted programming that represents an early manifestation of "material disengagement", wherein practitioners orchestrate code production and manipulation, mediated through AI, while maintaining selective and strategic oversight.