In 2025, coding agents have seen a very rapid adoption. Coding agents leverage Large Language Models (LLMs) in ways that are markedly different from LLM-based code completion, making their study critical. Moreover, unlike LLM-based completion, coding agents leave visible traces in software repositories, enabling the use of MSR techniques to study their impact on SE practices. This paper documents the promises, perils, and heuristics that we have gathered from studying coding agent activity on GitHub.

翻译：2025年，编码智能体经历了极为迅速的普及。编码智能体以显著不同于基于LLM的代码补全方式利用大语言模型（LLMs），这使得对其的研究至关重要。此外，与基于LLM的补全不同，编码智能体在软件仓库中留下了可见的痕迹，从而能够运用MSR技术来研究其对软件工程实践的影响。本文记录了我们在研究GitHub上编码智能体活动过程中所收集到的承诺、风险与启发式方法。