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.
Abstract
Vibe coding is a software development practice in which developers state intent in natural language and large language models generate code. It is often framed as one-shot prompting, but the evidence describes an intent-driven, iterative workflow whose outcomes depend on how generated code is evaluated and governed. Knowledge of how vibe coding is defined, practiced, and governed is scattered across academic and practitioner sources, and, to our knowledge, existing reviews have not yet integrated both evidence streams. We conducted a multivocal literature review of peer-reviewed and grey literature following established guidelines. Searches spanned 2022 to October 2025. After screening, credibility assessment, and snowballing, 47 sources were retained (28 peer-reviewed and 19 grey) and analyzed through descriptive mapping and thematic synthesis across eight research questions. Vibe coding is consistently described as an iterative generation-evaluation-revision loop rather than a one-shot activity, and developer work shifts from writing code towards specification, supervision, and validation. Short-term productivity and time-to-prototype gains are reported in 21 of 47 sources (45%), while evidence on maintainability, long-term quality, and safeguard effectiveness remains limited. Evidence 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. This is one of the first reviews to integrate peer-reviewed and grey literature on vibe coding under a single documented protocol. Future work should evaluate safeguard effectiveness, study session-level dynamics and long-term maintainability, and test vibe coding in production, data-intensive, and safety-critical settings.
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, F. Lazzari et al.· 0 citations
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, and provides practical risk mitigation recom...
A. Jauhari, Fahmi Fathullah, I. Permana et al.· International Journal for Sc...· 0 citations
The literature suggests that AI coding assistants can support productivity, but current evidence does not yet support simple, context-independent conclusions, so a structured basis for making productivity evidence more interpretable, multidimensional, and comparable across studies is provided.
Generative AI-assisted coding may have introduced a meaningful opportunity for educational researchers with no programming experience to extend their work beyond typical scholarly outputs by creating functional software. Translating research into applications has historically required significant coding expertise, but...
M. Littrell· International Journal of AI...· 0 citations
AI interviewers should be treated as a complementary option for short, focused, low-risk studies, with protocol design, privacy guidance, artifact validation, and human oversight, but do not establish completion rates, time savings, summary fidelity, or equivalence to human-conducted interviews.
Rohit Gheyi, D. Albuquerque, Márcio Ribeiro et al.· arXiv.org· 0 citations
A high-quality benchmark of 1,000 code refinement instances from 328 Python, Java, and JavaScript repositories that focused on one of the most challenging code refinement scenarios that strictly requires repository-level knowledge reasoning, and a straightforward method, RepoRefiner, which retrieves repository-level co...
Ke Wang, Peng Lan, Jia-Kun Liu et al.· ACM Transactions on Software...· 1 citation
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