On the Possibility of Breaking Copyleft Licenses When Reusing Code Generated by ChatGPT

TL;DR

This study investigates how AI code assistants like ChatGPT may inadvertently reproduce copyleft-licensed code, risking license violations, and explores how different generation settings affect this risk.

Contribution

It provides the first large-scale analysis of copyleft license reproduction in ChatGPT-generated code, highlighting factors influencing this phenomenon.

Findings

Larger context increases copyleft code reproduction

Higher temperature settings reduce the risk

Over 70,000 method implementations analyzed

Abstract

AI assistants can help developers by recommending code to be included in their implementations (e.g., suggesting the implementation of a method from its signature). Although useful, these recommendations may mirror copyleft code available in public repositories, exposing developers to the risk of reusing code that they are allowed to reuse only under certain constraints (e.g., a specific license for the derivative software). This paper presents a large-scale study about the frequency and magnitude of this phenomenon in ChatGPT. In particular, we generate more than 70,000 method implementations using a range of configurations and prompts, revealing that a larger context increases the likelihood of reproducing copyleft code, but higher temperature settings can mitigate this issue.