Vibe Coding in Practice: Flow, Technical Debt, and Guidelines for Sustainable Use

2025-12-11

AI Summary

While Vibe Coding (VC) accelerates prototyping and MVP development, it introduces technical debt—including architectural inconsistencies, security vulnerabilities, and elevated maintenance costs—due to flawed workflows, biased training data, and opaque design principles. This paper presents the first systematic analysis of three core causal mechanisms underlying VC-induced technical debt. Through industrial case studies, model capability boundary assessment, technical debt auditing, and design provenance diagnosis, we propose a “process–model–platform” co-governance framework. We further derive 12 actionable, implementation-ready guidelines for sustainable VC adoption. Our key contribution lies in attributing VC risks to structural deficiencies in the design process and establishing a long-term maintainability-oriented governance paradigm. This work provides both theoretical foundations and practical pathways for the engineering-scale deployment of generative AI–assisted programming.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Social and technical mechanisms of refusal for web technologies and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Vibe Coding (VC) is a form of software development assisted by generative AI, in which developers describe the intended functionality or logic via natural language prompts, and the AI system generates the corresponding source code. VC can be leveraged for rapid prototyping or developing the Minimum Viable Products (MVPs); however, it may introduce several risks throughout the software development life cycle. Based on our experience from several internally developed MVPs and a review of recent industry reports, this article analyzes the flow-debt tradeoffs associated with VC. The flow-debt trade-off arises when the seamless code generation occurs, leading to the accumulation of technical debt through architectural inconsistencies, security vulnerabilities, and increased maintenance overhead. These issues originate from process-level weaknesses, biases in model training data, a lack of explicit design rationale, and a tendency to prioritize quick code generation over human-driven iterative development. Based on our experiences, we identify and explain how current model, platform, and hardware limitations contribute to these issues, and propose countermeasures to address them, informing research and practice towards more sustainable VC approaches.

Problem

• Analyzes flow-debt tradeoffs in AI-assisted software development
• Identifies technical debt risks from rapid AI code generation
• Proposes sustainable guidelines to mitigate AI coding issues

Innovation

• AI-assisted natural language code generation
• Analyzing flow-debt tradeoffs in AI coding
• Proposing sustainable guidelines for AI development