[2603.25146] Factors Influencing the Quality of AI-Generated Code: A Synthesis of Empirical Evidence

Computer Science > Software Engineering arXiv:2603.25146 (cs) [Submitted on 26 Mar 2026] Title:Factors Influencing the Quality of AI-Generated Code: A Synthesis of Empirical Evidence Authors:Vehid Geruslu, Zulfiyya Aliyeva, Eray Tüzün View a PDF of the paper titled Factors Influencing the Quality of AI-Generated Code: A Synthesis of Empirical Evidence, by Vehid Geruslu and 2 other authors View PDF Abstract:Context: The rapid adoption of AI-assisted code generation tools, such as large language models (LLMs), is transforming software development practices. While these tools promise significant productivity gains, concerns regarding the quality, reliability, and security of AI-generated code are increasingly reported in both academia and industry. --Objective: This study aims to systematically synthesize existing empirical evidence on the factors influencing the quality of AI-generated source code and to analyze how these factors impact software quality outcomes across different evaluation contexts. --Method: We conducted a systematic literature review (SLR) following established guidelines, supported by an AI-assisted workflow with human oversight. A total of 24 primary studies were selected through a structured search and screening process across major digital libraries. Data were extracted and analyzed using qualitative, pattern-based evidence synthesis. --Results: The findings reveal that code quality in AI-assisted development is influenced by a combination of human fac...