What type of study is this?

This is a Experimental Study study.

September 23, 2025

AI-Generated Code Detection and Quality/Security Assessment Framework

Key Points

The framework accurately determines whether code is authored by a human or AI, enhancing security measures.
Quality and security are assessed together through a combined risk score, addressing coding vulnerabilities effectively.
Stylometric analysis, abstract syntax trees, and machine learning classifiers form the foundation of this assessment method.
Utilizing this framework may improve production code reliability, highlighting the resilience against code manipulation.

Abstract

Artificial Intelligence (AI) tools like GitHub Copilot and ChatGPT are changing software development by automating routine coding tasks and speeding up delivery times. However, using AI-generated code in production environments comes with significant challenges. These include verifying the source of the code, ensuring its quality, and managing vulnerabilities. This paper proposes a flexible, multi-layered framework to (i) determine whether a code snippet is created by AI or a human and (ii) evaluate its quality and security using a combined risk score. The framework uses stylometric analysis, abstract syntax tree (AST) patterns, and static application security testing (SAST) results alongside machine learning classifiers. It aims to be effective against code obfuscation, reformatting, and any edits made afterward.

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Cite This Study

Saiyad Uzaib (Tue,) studied this question.

synapsesocial.com/papers/68d4724731b076d99fa6a822 https://doi.org/https://doi.org/10.15662/ijmserh.2025.1303074

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