Dec 2025· arXiv.org· Vol abs/2512.11922· 4 citations· 29 references
Computer Science
TL;DR
This article analyzes the flow-debt tradeoffs associated with VC and identifies and explains how current model, platform, and hardware limitations contribute to these issues, and proposes countermeasures to address them, informing research and practice towards more sustainable VC approaches.
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.
This state-of-the-art review assembles that evidence across a cross-disciplinary corpus spanning software engineering, human-computer interaction, labour economics, security research, governance, and education, finding the early benchmarks saturated but task-level capability uneven.
D. Michels, Mutaz Abu Ghazaleh, Francois Lazzari et al.· 0 citations
This study compares the structural quality of code produced by three widely adopted vibe coding tools --- Lovable, v0, and Replit --- starting from a single generation prompt and suggests that choosing between vibe coding tools involves structural trade-offs that go beyond perceived productivity.
A codepath-aware governance framework for AI-assisted engineering in regulated codebases, with emphasis on financial services, payments, healthcare, and other domains where software changes may affect legal, operational, privacy, and audit obligations is developed.
Ashutosh Pal· International journal of com...· 0 citations
Enterprise software requires specification governance to transform probabilistic AI generation into deterministic, auditable engineering, and the SGRM framework is introduced, which defines four-component specification contracts, constrains stochastic generation via deterministic validation, and integrates generation, verification, and governance into a closed-loop architecture.
The literature on vibe coding has grown rapidly; however, it remains fragmented and is largely dominated by industry reports, leaving its position relative to traditional manual programming and low-code development insufficiently examined. This gap makes it difficult for both researchers and practitioners to determine when vibe coding is appropriate and what risks should be anticipated. Purpose: This study aims to systematically map the current landscape of vibe coding, develop a comparative framework against manual and low-code software development approaches, and propose practical risk mitigation recommendations for software development practitioners. Methodology: A Systematic Literature Review (SLR) was conducted following the PRISMA protocol. Relevant publications from 2023 to 2026 were retrieved from IEEE Xplore, ACM Digital Library, Springer, ScienceDirect, and arXiv, resulting in 61 studies that were analyzed using thematic analysis. Findings: The results indicate that vibe coding can accelerate software prototyping by approximately 40–60% compared with manual development. However, it introduces a verification bottleneck by shifting developers' workload from code implementation to quality assurance and validation. Compared with low-code development, vibe coding provides greater flexibility in expressing user intent but exhibits lower output predictability. In comparison with manual development, it offers significant gains in development speed while sacrificing architectural control and code security, thereby increasing the risks of technical skill degradation, hidden security vulnerabilities, and accumulated technical debt. Implications: The findings provide practical guidance for software development teams in identifying project phases that are suitable for extensive adoption of vibe coding and those that still require manual architectural review. The study also emphasizes the importance of integrating security auditing and technical debt monitoring into AI-assisted software development workflows. Originality/Value: The novelty of this study lies in its explicit comparative framework, which systematically positions vibe coding alongside manual and low-code development across six technical dimensions, extending previous studies that have generally examined vibe coding in isolation.
A. Jauhari, Fahmi Fathullah, I. Permana et al.· International Journal for Sc...· 0 citations
This is one of the first reviews to integrate peer-reviewed and grey literature on vibe coding under a single documented protocol and is strongest for prototyping and user-interface work and weakest for production, data-intensive, and safety-critical use, and tool visibility does not imply effectiveness.
Shahbaz Siddeeq, Muhammad Waseem, Kai-Kristian Kemell et al.· arXiv.org· 0 citations
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