Vibe Coding in Software Development: A Multivocal Literature Review
Shahbaz Siddeeq, Muhammad Waseem, Kai-Kristian Kemell, Mika Saari, Jussi Rasku, Pekka Abrahamsson
Vibe coding is an AI-assisted software development approach in which developers express intent in natural language and large language models generate code. To our knowledge, no review has synthesized academic and practitioner sources under a common protocol. We conducted a multivocal literature review of publications from January 2022 to 9 September 2026, identified through searches in October 2025 and September 2026. After assessing source quality and credibility, we analyzed 266 sources (76 peer-reviewed, 190 grey) across eight research questions: 146 were direct, 93 adjacent, and 27 background. Presence and outcome findings receive reporting-support and evidence-strength labels, respectively. The second author conducted twelve checks; pre-reconciliation agreement was low in several, but substantial for the final directness rule and moderate for the outcome fields, which were checked in full. Sources describe a generation-evaluation-revision loop, with developer work shifting from writing code toward directing, supervising, and validating. Productivity is reported in 109 of the 159 sources contributing to the outcome question, of which 22 provide assertions only; these counts indicate reporting, not effect size. Most comparable sources report short-term productivity gains and long-term maintainability costs, but both receive Weak evidence-strength labels because some comparable sources report opposing directions. Short-term quality and defects are labeled Weak. Long-term productivity, long-term quality, and short-term reproducibility are labeled Moderate. Prototyping is reported more often than production use, and user-interface work more often than data and machine-learning work, but task-domain evidence is sparse. Future work should evaluate safeguards, session-level dynamics, long-term maintainability, and production use.
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