Skip to content

Vibe Coding in Software Development: A Multivocal Literature Review

Jul 2026 · arXiv.org · Vol abs/2607.21652 · 0 citations · 53 references
Computer Science

TL;DR

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.

View source

Similar papers

Review Aug 2026

Vibe Coding: Practice, Performance, Productivity, and Risk -A State-of-the-Art Review

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
Review Open access Jul 2026

A Systematic Analysis of AI-Assisted Vibe Coding in Software Development: Opportunities, Challenges, and Risks

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. · 0 citations
Review

Fragmented Markers, Mixed Results: A Systematic Review of AI Coding Assistants and Developer Productivity

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.

Annemarie Wittig, Norbert Siegmund · 0 citations
Open access Aug 2026

Educational Research-Driven Software Development

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 · 0 citations
Jul 2026

AI-Conducted Interviews in Empirical Software Engineering: An Experience Report

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. · 0 citations
Review Aug 2026

Code Refinement with Repository Context: How Far are We?

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. · 1 citation

Related blog posts

Microsoft Research Blog Oct 15, 2026

Proof-based general-purpose computing with Magnetar

Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.

MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.