  This study aims to assess the performance of two advanced Large Language
Models (LLMs), GPT-3.5 and GPT-4, in the task of code clone detection. The
evaluation involves testing the models on a variety of code pairs of different
clone types and levels of similarity, sourced from two datasets: BigCloneBench
(human-made) and GPTCloneBench (LLM-generated). Findings from the study
indicate that GPT-4 consistently surpasses GPT-3.5 across all clone types. A
correlation was observed between the GPTs' accuracy at identifying code clones
and code similarity, with both GPT models exhibiting low effectiveness in
detecting the most complex Type-4 code clones. Additionally, GPT models
demonstrate a higher performance identifying code clones in LLM-generated code
compared to humans-generated code. However, they do not reach impressive
accuracy. These results emphasize the imperative for ongoing enhancements in
LLM capabilities, particularly in the recognition of code clones and in
mitigating their predisposition towards self-generated code clones--which is
likely to become an issue as software engineers are more numerous to leverage
LLM-enabled code generation and code refactoring tools.
