This study compares the structural quality of code produced by three widely adopted vibe coding tools --- Lovable, v0, and Replit --- starting from a single generation prompt and suggests that choosing between vibe coding tools involves structural trade-offs that go beyond perceived productivity.
Abstract
The use of AI agents for automatic code generation has become increasingly common in software development. However, concerns remain about the quality of the generated code, including aspects of maintainability, readability, and long-term evolution. This study compares the structural quality of code produced by three widely adopted vibe coding tools --- Lovable, v0, and Replit --- starting from a single generation prompt. We generate three independent projects per tool, totalling nine web applications, and submit them to static analysis with SonarQube. We collect metrics such as the number of issues, severity distribution, estimated remediation effort, cyclomatic and cognitive complexity, and code duplication. Preliminary results show that the tools exhibit distinct qualitative profiles: Lovable concentrates issues of lower severity but presents a substantially higher density of code smells per KLOC, while v0 and Replit produce more code with more aggressive severity profiles. These findings suggest that choosing between vibe coding tools involves structural trade-offs that go beyond perceived productivity.
It is observed that generated code often omits basic input validation or memory-safety checks, which can lead to overflows, resource exhaustion, or other reliability/security issues, and even the largest models frequently make simple mistakes.
Rodrigo Pato Nogueira, Marco Vieira, João R. Campos· 0 citations
Recently, Developers have been relying on AI tools to support them in their daily work by generating code. While the use of large language model-based AI tools has improved productivity, the quality of the generated code wasn't always optimal. In a lot of cases, the code includes design issues known as code smells, which negatively impact readability, maintainability, and future development. This paper investigates these issues in AI-generated Java code, with a focus on common object-oriented problems such as switch statements, temporary fields, and refused bequest. A structured approach is proposed that combines static analysis tools with explainable AI techniques to better understand why these problems appear. Based on the realized insights, prompts are optimized to guide the AI model towards generating cleaner and more structured code. The results showed clear improvement after the prompt optimization, where the number of detected code smells was reduced by 66%, and completely removed (100% reduction) in some cases. Overall, the study showed that improving prompt design, supported by explainable analysis, can significantly enhance the quality of AI-generated code.
Y. Younes, Yousef Elsheikh· IEEE Jordan Conference on Ap...· 0 citations
An automated, multi-dimensional evaluation framework for C# code generation, applying it to four state-of-the-art LLMs: GPT, Gemini, Claude, and Grok is presented and a substantial gap between correctness and quality attributes is revealed.
Seyed Mohammad Mahdi Ghalandarian, Majid Bazargani, Masoumeh Taromirad· 0 citations
This systematic review presents an analysis of the "Vibe Coding" methodology — a contemporary approach to the iterative software development process using Large Language Models (LLMs). Code generation tools are transforming software development by enabling programmers to formulate tasks and describe the desired behavior of software in natural language, while LLMs generate source code corresponding to these requests. The review systematizes current methodologies for the use of LLMs, highlights application examples, evaluates the effectiveness of generated code, discusses emerging challenges, and outlines future development trends of the technology. The aim of this work is to provide a comprehensive understanding of the capabilities and limitations of Vibe Coding as a transformational methodology in software engineering.
A. Dzhonov, S. M. Avdoshin· INFORMACIONNYE TEHNOLOGII· 0 citations
Investigating LLMs as metric-driven refactoring assistants rather than code generators suggests that while LLMs are valuable assistants for structural improvement, their interventions require careful monitoring to avoid unintended trade-offs.
Tindwende Thierry Sawadogo, Fadel Touré· International Conference on...· 0 citations
AI-generated C++ code has a distinct quality profile, showing higher rates of interface and coupling burdens, copy and allocation overheads, and a reliance on explicit loops over optimized standard APIs, which translates into tangible downstream costs, including increased review effort and a 5-8% increase in compute resource consumption.
Michael Tran, Fred Lewis, Kun Yang et al.· 1 citation
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