Jul 2026· European Conference on Artificial Intelligence· pp. 1-8· 0 citations· 13 references
Abstract
Vibe coding is an emerging approach in which developers and non-technical users describe desired functionalities in natural language, delegating actual code generation to an artificial intelligence model. This paper presents a structured case study of vibe coding through a practical experiment, using Visual Studio Code and Anthropic's Claude model to build a functional web application. We document how the vibe coding workflow operates in practice, compare the estimated development time against a conventional manual implementation of the same application, and examine the security posture of the resulting software through a structured audit. While the artificial intelligence (AI) assisted process demonstrated a notable reduction in implementation time - illustrative of a single-user, single-application scenario and not generalizable beyond it - the security audit identified several categories of vulnerabilities in the generated codebase, suggesting that vibe coding may not reliably produce secure software in this case study. The study further identifies inherent risks of this paradigm, including potential exposure of sensitive data and the difficulty of auditing automatically generated code. We also propose a conceptual clarification between vibe coding and agentic coding paradigms. We conclude that vibe coding democratizes access to software development but requires rigorous oversight frameworks to be adopted responsibly.
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
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
This systematic review presents an analysis of the "Vibe Coding" methodology — a contemporary approach to the iterative software development process using Large Language Models (LLMs). Code generation tools are transforming software development by enabling programmers to formulate tasks and describe the desired behavior of software in natural language, while LLMs generate source code corresponding to these requests. The review systematizes current methodologies for the use of LLMs, highlights application examples, evaluates the effectiveness of generated code, discusses emerging challenges, and outlines future development trends of the technology. The aim of this work is to provide a comprehensive understanding of the capabilities and limitations of Vibe Coding as a transformational methodology in software engineering.
A. Dzhonov, S. M. Avdoshin· INFORMACIONNYE TEHNOLOGII· 0 citations
A student survey study is presented that examines perceptions of LLM output understanding, validation effort, trust and the perceived usefulness of vibe modeling across several AI-assisted development scenarios to inform future studies for trustworthy and explainable AI-based software engineering via vibe modeling.
Shalini Chakraborty, M. Mittermaier, J. Michael· 0 citations
The risk is not tied to any particular model but rather to the task category, and organizations should ask not which tool to trust, but instead whether LLM-generated automation code should be deployed without review.
It is concluded that AI meaningfully augments developer productivity but does not yet demonstrably improve satisfaction or earnings, and that a hybrid human-AI model, supported by governance and training, remains the most defensible direction for application development.
Perseus Bhavnagri· International Journal for Re...· 0 citations
