Vibe Coding in Software Development: A Multivocal Literature Review

2026-07-22

AI Summary

This study addresses the fragmented understanding of “vibe coding” by systematically synthesizing knowledge dispersed across academic and practitioner literature. Employing a unified protocol and a multi-voiced literature review methodology, it analyzes 47 peer-reviewed and gray literature sources from 2022 to 2025, revealing that vibe coding is fundamentally an intent-driven iterative cycle of generation, evaluation, and refinement. The findings indicate that 45% of the reviewed works report short-term productivity gains, with the strongest empirical support for its efficacy in prototyping and UI development. However, significant evidence gaps persist regarding its applicability in production-grade, data-intensive, and safety-critical contexts. This work provides a structured empirical foundation for understanding the evolving role of developers and delineating the appropriate boundaries for vibe coding adoption.

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.

Problem

• vibe coding
• software development
• large language models
• code generation
• multivocal literature review

Innovation

• vibe coding
• multivocal literature review
• intent-driven development
• LLM-assisted programming
• iterative code generation