标题： 软件工程（SE）3.0中AI队友的崛起：自主编码代理如何重塑软件工程 显示英文标题

标题： The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering

摘要： 软件工程的未来——SE 3.0——随着AI队友的兴起而展开：自主的、目标驱动的系统与人类开发者协作。 其中，自主编码代理尤其具有变革性，现在正在大规模地主动发起、审查和演进代码。 本文介绍了AIDev，这是第一个大规模数据集，记录了这些代理在现实中的运作情况。 涵盖五家领先代理——OpenAI Codex、Devin、GitHub Copilot、Cursor和Claude Code——在61,000个仓库和47,000名开发者中产生的超过456,000个拉取请求，AIDev为研究软件开发中的自主队友提供了前所未有的实证基础。 与之前主要理论化AI原生软件工程崛起的工作不同，AIDev提供了结构化的开放数据，以支持基准测试、代理准备度、优化、协作建模和AI治理方面的研究。 该数据集包含关于PR、作者、审查时间线、代码更改和集成结果的丰富元数据——使探索超越SWE-bench等合成基准成为可能。 例如，尽管代理通常在速度上优于人类，但它们的PR被接受的频率较低，揭示了信任和效用之间的差距。 此外，虽然代理加快了代码提交——一名开发者在三天内提交的PR数量相当于他们三年的总量——但这些PR在结构上更简单（通过代码复杂度指标）。 我们设想AIDev是一个不断发展的资源：可扩展、可分析，并准备好供SE和AI社区使用。 基于现实世界的证据，AIDev使新一代关于AI原生工作流程的研究成为可能，并支持构建下一代共生的人机协作。 该数据集可在https://github.com/SAILResearch/AI_Teammates_in_SE3公开获取。 > AI代理，代理AI，编码代理，代理编码，软件工程 代理 显示英文摘要

摘要： The future of software engineering--SE 3.0--is unfolding with the rise of AI teammates: autonomous, goal-driven systems collaborating with human developers. Among these, autonomous coding agents are especially transformative, now actively initiating, reviewing, and evolving code at scale. This paper introduces AIDev, the first large-scale dataset capturing how such agents operate in the wild. Spanning over 456,000 pull requests by five leading agents--OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code--across 61,000 repositories and 47,000 developers, AIDev provides an unprecedented empirical foundation for studying autonomous teammates in software development. Unlike prior work that has largely theorized the rise of AI-native software engineering, AIDev offers structured, open data to support research in benchmarking, agent readiness, optimization, collaboration modeling, and AI governance. The dataset includes rich metadata on PRs, authorship, review timelines, code changes, and integration outcomes--enabling exploration beyond synthetic benchmarks like SWE-bench. For instance, although agents often outperform humans in speed, their PRs are accepted less frequently, revealing a trust and utility gap. Furthermore, while agents accelerate code submission--one developer submitted as many PRs in three days as they had in three years--these are structurally simpler (via code complexity metrics). We envision AIDev as a living resource: extensible, analyzable, and ready for the SE and AI communities. Grounding SE 3.0 in real-world evidence, AIDev enables a new generation of research into AI-native workflows and supports building the next wave of symbiotic human-AI collaboration. The dataset is publicly available at https://github.com/SAILResearch/AI_Teammates_in_SE3. > AI Agent, Agentic AI, Coding Agent, Agentic Coding, Software Engineering Agent