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

TL;DR

Vibe Coding, an AI-assisted software development approach, offers rapid prototyping but risks accumulating technical debt and architectural issues, requiring guidelines for sustainable use.

Contribution

This paper analyzes the flow-debt tradeoffs in Vibe Coding, identifies root causes of technical debt, and proposes countermeasures for sustainable AI-assisted development.

Findings

Flow-debt tradeoff impacts code quality and maintainability.

Model and platform limitations contribute to technical debt.

Countermeasures can mitigate risks and improve sustainability.

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.