Coding remains one of the most fundamental modes of interaction between humans and machines. With the rapid advancement of Large Language Models (LLMs), code generation capabilities have begun to significantly reshape programming practices. This development prompts a central question: Have LLMs transformed code style, and how can such transformation be characterized? In this paper, we present a pioneering study that investigates the impact of LLMs on code style, with a focus on naming conventions, complexity, maintainability, and similarity. By analyzing code from over 20,000 GitHub repositories linked to arXiv papers published between 2020 and 2025, we identify measurable trends in the evolution of coding style that align with characteristics of LLM-generated code. For instance, the proportion of snake_case function names in Python code increased from 40.7% in Q1 2023 to 49.8% in Q3 2025. Furthermore, we investigate how LLMs approach algorithmic problems by examining their reasoning processes. Our experimental results may provide the first large-scale empirical evidence that LLMs affect real-world programming style. We release all the experimental dataset and source code at: https://github.com/ignorancex/LLM_code

翻译：编码仍然是人与机器之间最基本的交互模式之一。随着大型语言模型（LLMs）的快速发展，代码生成能力已开始显著重塑编程实践。这一进展引出了一个核心问题：LLMs是否改变了代码风格？这种转变应如何表征？本文提出了一项开创性研究，探究LLMs对代码风格的影响，重点关注命名规范、复杂度、可维护性和相似性。通过分析2020年至2025年间发布的arXiv论文关联的20,000余个GitHub仓库代码，我们识别出与LLM生成代码特征相符的编码风格演化趋势。例如，Python代码中snake_case函数名的比例从2023年第一季度的40.7%上升至2025年第三季度的49.8%。此外，我们通过考察LLMs的推理过程，探究其处理算法问题的方式。实验结果可能首次提供了LLMs影响现实世界编程风格的大规模实证证据。我们在https://github.com/ignorancex/LLM_code 公开了全部实验数据集与源代码。