In the first half of 2025, coding agents have emerged as a category of development tools that have very quickly transitioned to the practice. Unlike ''traditional'' code completion LLMs such as Copilot, agents like Cursor, Claude Code, or Codex operate with high degrees of autonomy, up to generating complete pull requests starting from a developer-provided task description. This new mode of operation is poised to change the landscape in an even larger way than code completion LLMs did, making the need to study their impact critical. Also, unlike traditional LLMs, coding agents tend to leave more explicit traces in software engineering artifacts, such as co-authoring commits or pull requests. We leverage these traces to present the first large-scale study (129,134 projects) of the adoption of coding agents on GitHub, finding an estimated adoption rate of 15.85%--22.60%, which is very high for a technology only a few months old--and increasing. We carry out an in-depth study of the adopters we identified, finding that adoption is broad: it spans the entire spectrum of project maturity; it includes established organizations; and it concerns diverse programming languages or project topics. At the commit level, we find that commits assisted by coding agents are larger than commits only authored by human developers, and have a large proportion of features and bug fixes. These findings highlight the need for further investigation into the practical use of coding agents.

翻译：2025年上半年，编码智能体已迅速从概念验证阶段过渡到实际应用，成为一类新兴的开发工具。与Copilot等"传统"代码补全大语言模型不同，Cursor、Claude Code或Codex等智能体具有高度自主性，能够根据开发者提供的任务描述直接生成完整的拉取请求。这种新型工作模式预计将比代码补全大语言模型带来更深远的影响，因此研究其实际影响至关重要。与传统大语言模型相比，编码智能体往往会在软件工程制品中留下更明确的痕迹，例如以共同作者身份参与提交或拉取请求。我们利用这些痕迹开展了首次针对GitHub编码智能体采用情况的大规模研究（涵盖129,134个项目），发现其采用率估计已达15.85%--22.60%——对于仅出现数月的技术而言这一比例极高且仍在持续增长。通过对已识别采用者的深入分析，我们发现采用范围广泛：涵盖所有项目成熟度阶段；包含成熟组织机构；涉及多种编程语言与项目主题。在提交层面，编码智能体辅助生成的提交比纯人工提交规模更大，且功能实现与错误修复占比较高。这些发现表明，亟需对编码智能体的实际应用开展进一步研究。