mlsanityData to 14 Oct 2026

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

arXiv 2507.21928 ↗First posted 29 Jul 2025cs.SE cs.AI cs.HCcited by 13

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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.

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

30 of 141 references.

References · 30

  1. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  2. 2024A Survey on Large Language Models for Code Generationcited by 658
  3. 2017A Survey of Machine Learning for Big Code and Naturalnesscited by 221
  4. 2022Do Users Write More Insecure Code with AI Assistants?cited by 192
  5. 2022GitHub Copilot AI pair programmer: Asset or Liability?cited by 152
  6. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 126
  7. 2019Pythia: AI-assisted Code Completion Systemcited by 55
  8. 2021Hybrid Intelligencecited by 55
  9. 2024Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forwardcited by 45
  10. 2025Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AIcited by 42
  11. 2025Vibe coding: programming through conversation with artificial intelligencecited by 29
  12. 2021Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Studycited by 21
18 more
  1. 2024The Current Challenges of Software Engineering in the Era of Large Language Modelscited by 21
  2. 2024Prompts Are Programs Too! Understanding How Developers Build Software Containing Promptscited by 19
  3. 2024Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teamscited by 16
  4. 2025Conversational AI as a Coding Assistant: Understanding Programmers' Interactions with and Expectations from Large Language Models for Codingcited by 11
  5. 2025DBox: Scaffolding Algorithmic Programming Learning through Learner-LLM Co-Decompositioncited by 9
  6. 2023Validating AI-Generated Code with Live Programmingcited by 8
  7. 2024Transforming Software Development with Generative AI: Empirical Insights on Collaboration and Workflowcited by 6
  8. 2023Artificial Intelligence Impact On The Labour Force -- Searching For The Analytical Skills Of The Future Software Engineerscited by 4
  9. 2024Natural Language-Oriented Programming (NLOP): Towards Democratizing Software Creationcited by 4
  10. 2021LitterBox: A Linter for Scratch Programscited by 3
  11. 2023Unravelling Responsibility for AIcited by 3
  12. 2024Enhancing SQL Query Generation with Neurosymbolic Reasoningcited by 3
  13. 2024The Impact of Generative Artificial Intelligence on Ideation and the performance of Innovation Teams (Preprint)cited by 3
  14. 2024DIDUP: Dynamic Iterative Development for UI Prototypingcited by 2
  15. 2019The Prolog debugger and declarative programmingcited by 1
  16. 2024Generative AI Enhances Team Performance and Reduces Need for Traditional Teamscited by 1
  17. 2025The Role of Generative AI in Strengthening Secure Software Coding Practices: A Systematic Perspectivecited by 1
  18. 2025User-Centered Design with AI in the Loop: A Case Study of Rapid User Interface Prototyping with "Vibe Coding"cited by 1

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Citing papers per year: 2025: 2; 2026: 11.

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