From Prompting to Verification: How Experience Shapes Vibe Coding Practices

2026-05-23

AI Summary

This study investigates how users with varying levels of programming experience—non-programmers, novices, and professional developers—differ in their practices of vibe coding, a paradigm wherein code is generated via natural language prompts and validated through execution. Drawing on a mixed-methods survey of 162 participants, the research integrates quantitative and qualitative analyses to reveal distinct patterns in motivation, interaction strategies, and quality assurance behaviors: non-programmers prioritize accessibility, novices emphasize learning, and professionals seek efficiency. The work introduces the concept of a “perception–action gap,” highlighting that while users broadly recognize the risks associated with AI-generated code, their actual ability to verify its correctness remains heavily contingent on programming expertise. This finding underscores the limitations of vibe coding in achieving true code democratization.

Abstract

AI code generation tools have expanded software creation beyond professional developers, giving rise to vibe coding, a practice in which users generate software via natural-language prompts, evaluate outputs primarily by execution. Prior work has examined how AI code generation tools support programming tasks within specific user groups, typically professional developers, leaving open the question of how vibe coding practices differ across experience levels. We address this gap by surveying 162 vibe coders belonging to three user experience groups: non-coders, novices, and professional developers. Our results show that experience selectively shapes vibe coding. Reported experiences and perceptions of code quality are broadly similar across groups, with all three recognising both the strengths and limitations of vibe coding. In contrast, motivations, interaction styles, and quality assurance practices diverge with experience. Non-developers are most motivated by accessibility, novices emphasise learning and experimentation, and professionals use vibe coding more frequently in work-related contexts. We synthesise these findings as a perception--action gap: a general awareness of risks in AI-generated code is broadly distributed, but the capacity to evaluate, debug, and verify remains experience-dependent. We show that vibe coding is partially democratising as it broadens access to software creation without equally distributing the expertise to evaluate it.

Problem

• vibe coding
• AI code generation
• user experience
• code quality assurance
• non-professional developers

Innovation

• vibe coding
• AI code generation
• perception–action gap
• code verification
• programming experience