  Large language models (LLMs) have brought a paradigm shift to the field of
code generation, offering the potential to enhance the software development
process. However, previous research mainly focuses on the accuracy of code
generation, while coding style differences between LLMs and human developers
remain under-explored. In this paper, we empirically analyze the differences in
coding style between the code generated by mainstream Code LLMs and the code
written by human developers, and summarize coding style inconsistency taxonomy.
Specifically, we first summarize the types of coding style inconsistencies by
manually analyzing a large number of generation results. We then compare the
code generated by Code LLMs with the code written by human programmers in terms
of readability, conciseness, and robustness. The results reveal that LLMs and
developers have different coding styles. Additionally, we study the possible
causes of these inconsistencies and provide some solutions to alleviate the
problem.
