命名风格趋同，网络局部性持续：LLM 时代的 GitHub
Converging Naming Styles, Persistent Network Locality: GitHub in the LLM Era
AI总结：
本研究基于 GitHub 仓库数据，发现 LLM 时代命名风格总体趋同，但协作网络邻近的仓库仍保持局部相似性，表明聚合趋同与网络局部差异可共存。
AI中文摘要：
社会惯例通常通过社交网络中的重复互动而出现，使得共享实践能够与不同群体间的差异共存。大语言模型（LLM）引入了一种可能不同的协调结构：少数广泛使用的模型可以将社交上相距较远的用户暴露于相似的模式和建议之下。在这种共享技术影响下，广泛趋同是否会消除网络局部的差异仍不清楚。我们在软件开发领域研究这一问题，其中标识符命名风格提供了可观察的惯例，而基于 LLM 的工具已迅速普及。利用 2015 年至 2025 年 9 月间在六种编程语言中创建的公共 GitHub 仓库，我们用 27 个特征刻画命名风格，并考察其与检测到的 LLM 相关提交的关联、跨仓库创建队列的多样性，以及其与大规模协作网络中所有者邻近性的关系。检测到 LLM 相关提交的仓库倾向于使用更长的标识符，并且在几种语言中，更多地使用该语言中已流行的命名模式。我们还观察到近期创建队列的命名风格多样性较低，在几种语言中，约 2023-2024 年出现显著下降，尽管其时间和轨迹各不相同。与此同时，网络局部性持续存在：在六种语言中的五种中，即使是在近期更同质的队列中，所有者协作网络距离更近的仓库在命名风格上仍然更为相似。这些发现表明，总体趋同与网络局部差异可以共存，凸显了不仅需要考察文化差异还保留多少，还需要考察在广泛共享的 AI 系统时代，这种差异如何继续由人类社交关系所结构化。
英文摘要：
Social conventions often emerge through repeated interactions within social networks, allowing shared practices to coexist with variation across groups. Large language models (LLMs) introduce a potentially different coordination structure: a small number of widely used models can expose socially distant users to similar patterns and suggestions. Whether broad convergence under such shared technological influences eliminates network-local variation remains unclear. We examine this question in software development, where identifier naming styles provide observable conventions and LLM-based tools have rapidly diffused. Using public GitHub repositories created between 2015 and September 2025 across six programming languages, we characterize naming styles with 27 features and examine their association with detected LLM-related commits, their diversity across repository creation cohorts, and their relationship to owner proximity in a large-scale collaboration network. Repositories with detected LLM-related commits tend to use longer identifiers and, in several languages, make greater use of naming patterns already prevalent within the language. We also observe lower naming-style diversity in recent creation cohorts, with marked declines appearing around 2023-2024 in several languages, although their timing and trajectories differ. At the same time, network locality persists: in five of the six languages, repositories whose owners are closer in the collaboration network remain more similar in naming style even among recent, more homogeneous cohorts. These findings show that aggregate convergence and network-local variation can coexist, highlighting the need to examine not only how much cultural variation remains, but also how that variation continues to be structured by human social relationships in the era of widely shared AI systems.
