ChatGPT has shown great potential in the field of software engineering with its ability to generate code. Yet, ChatGPT’s ability to interpret code has been deemed unreliable and faulty, which causes concern for the platform’s ability to properly refactor code.  To confront this concern, we carried out a study to assess ChatGPT’s abilities and limitations in refactoring code. We divided the study into two parts: if ChatGPT will refactor code, and if the refactored code preserves the behavior of the original code segments. We focused our research specifically on eight quality attributes to use when prompting ChatGPT to refactor our dataset of 40 Java code segments. After collecting the refactored code segments from ChatGPT, as well as data on whether the behavior was preserved, we ran the refactored code through PMD, a source code analyzer, to find programming flaws. We also tested ChatGPT’s accuracy in generating commit messages for the refactored code and analyzed the difference between the results of each quality attribute. We conclude that ChatGPT is able to provide many useful refactoring changes that can improve the code quality which is crucial, but our results also show that ChatGPT can be unpredictable in its responses which threatens the reliability of ChatGPT. Additionally, we found that the quality attributes play a crucial role in the refactoring methods of ChatGPT, encouraging future research to explore ways of manipulating ChatGPT to only use specific methods and techniques in refactoring.