# Vibe Coding as a Reconfiguration of Intent Mediation in Software Development: Definition, Implications, and Research Agenda

arXiv: https://arxiv.org/abs/2507.21928 · HTML: https://mlsanity.com/paper/2507.21928/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 141 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 30 of 30)

- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024)
- [Pythia: AI-assisted Code Completion System](https://mlsanity.com/paper/1912.00742/) (2019)
- [Hybrid Intelligence](https://mlsanity.com/paper/2105.00691/) (2021)
- [Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward](https://mlsanity.com/paper/2406.07765/) (2024)
- [Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI](https://mlsanity.com/paper/2505.19443/) (2025)
- [Vibe coding: programming through conversation with artificial intelligence](https://mlsanity.com/paper/2506.23253/) (2025)
- [Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2103.09783/) (2021)
- [The Current Challenges of Software Engineering in the Era of Large Language Models](https://mlsanity.com/paper/2412.14554/) (2024)
- [Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts](https://mlsanity.com/paper/2409.12447/) (2024)
- [Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams](https://mlsanity.com/paper/2402.17721/) (2024)
- [Conversational AI as a Coding Assistant: Understanding Programmers' Interactions with and Expectations from Large Language Models for Coding](https://mlsanity.com/paper/2503.16508/) (2025)
- [DBox: Scaffolding Algorithmic Programming Learning through Learner-LLM Co-Decomposition](https://mlsanity.com/paper/2502.19133/) (2025)
- [Validating AI-Generated Code with Live Programming](https://mlsanity.com/paper/2306.09541/) (2023)
- [Transforming Software Development with Generative AI: Empirical Insights on Collaboration and Workflow](https://mlsanity.com/paper/2405.01543/) (2024)
- [Artificial Intelligence Impact On The Labour Force -- Searching For The Analytical Skills Of The Future Software Engineers](https://mlsanity.com/paper/2302.13229/) (2023)
- [Natural Language-Oriented Programming (NLOP): Towards Democratizing Software Creation](https://mlsanity.com/paper/2406.05409/) (2024)
- [LitterBox: A Linter for Scratch Programs](https://mlsanity.com/paper/2102.07440/) (2021)
- [Unravelling Responsibility for AI](https://mlsanity.com/paper/2308.02608/) (2023)
- [Enhancing SQL Query Generation with Neurosymbolic Reasoning](https://mlsanity.com/paper/2408.13888/) (2024)
- [The Impact of Generative Artificial Intelligence on Ideation and the performance of Innovation Teams (Preprint)](https://mlsanity.com/paper/2410.18357/) (2024)
- [DIDUP: Dynamic Iterative Development for UI Prototyping](https://mlsanity.com/paper/2407.08474/) (2024)
- [The Prolog debugger and declarative programming](https://mlsanity.com/paper/1906.04765/) (2019)
- [Generative AI Enhances Team Performance and Reduces Need for Traditional Teams](https://mlsanity.com/paper/2405.17924/) (2024)
- [The Role of Generative AI in Strengthening Secure Software Coding Practices: A Systematic Perspective](https://mlsanity.com/paper/2504.19461/) (2025)
- [User-Centered Design with AI in the Loop: A Case Study of Rapid User Interface Prototyping with "Vibe Coding"](https://mlsanity.com/paper/2507.21012/) (2025)

## Built on by (most cited first, 13 of 13)

- [Evaluating LLM-Based 0-to-1 Software Generation in End-to-End CLI Tool Scenarios](https://mlsanity.com/paper/2604.06742/) (2026)
- [Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaboration](https://mlsanity.com/paper/2509.10652/) (2025)
- [TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration](https://mlsanity.com/paper/2601.04544/) (2026)
- [From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents](https://mlsanity.com/paper/2607.08028/) (2026)
- [VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs](https://mlsanity.com/paper/2510.00031/) (2025)
- [Stand-Alone Complex or Vibercrime? Exploring the adoption and innovation of GenAI tools, coding assistants, and agents within cybercrime ecosystems](https://mlsanity.com/paper/2603.29545/) (2026)
- [Is Vibe Coding the Future? An Empirical Assessment of LLM Generated Codes for Construction Safety](https://mlsanity.com/paper/2604.12311/) (2026)
- [Automating Formal Verification with Agent-Guided Tree Search](https://mlsanity.com/paper/2605.27485/) (2026)
- [Toward Vibe Medicine: A Self-Evolving Multi-Agent Framework for Clinical Decision Support](https://mlsanity.com/paper/2606.15504/) (2026)
- [Where Did the Variability Go? From Vibe Coding to Product Lines by Regeneration](https://mlsanity.com/paper/2606.19042/) (2026)
- [Plainbook: Data Science, in Plain Language](https://mlsanity.com/paper/2607.05717/) (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)
- [It Is Not My Code Anymore](https://mlsanity.com/paper/2609.09022/) (2026)

## Abstract (arXiv)

Software development is undergoing a fundamental transformation as vibe coding becomes widespread, with large portions of contemporary codebases now being generated by Artificial Intelligence (AI). The disconnect between rapid adoption and limited conceptual understanding highlights the need for an inquiry into this emerging paradigm. Drawing on an intent perspective and historical analysis, we define vibe coding as a software development paradigm where humans and Generative AI (GenAI) engage in collaborative flow to co-create software artifacts through natural language dialogue, shifting the mediation of developer intent from deterministic instruction to probabilistic inference. By intent mediation, we refer to the fundamental process through which developers translate their conceptual goals into representations that computational systems can execute. Our results show that vibe coding redistributes epistemic labor between humans and machines, shifting expertise from technical implementation toward collaborative orchestration. We identify key opportunities, including democratization, acceleration, and systemic leverage, alongside risks such as black-box codebases, responsibility gaps, and ecosystem bias. We conclude with a research agenda spanning human-, technology-, and organization-centered directions to guide future investigations of this paradigm.