首次实证分析AI代码代理在移动端开源项目中的表现差异。
On the Adoption of AI Coding Agents in Open-source Android and iOS Development
- 分析2901个AI生成的代码提交，比较安卓与iOS平台接受率差异。
- 安卓项目接受率71%高于iOS的63%，常规任务更易被采纳。
- 发现安卓项目效率先升后降，为智能开发系统设计提供依据。
AI代码代理正日益参与软件开发，但其对移动开发的影响尚缺乏实证研究。本文首次对开源移动端项目中代理生成代码进行类别级实证分析。基于AIDev数据集，我们分析了193个经验证的安卓和iOS开源GitHub仓库中2,901个由AI撰写的拉取请求（PR）的接受行为，涵盖平台、代理及任务类别。结果表明，安卓项目收到的AI生成PR数量是iOS的两倍，且接受率更高（71%对比63%），安卓平台存在显著代理间差异。在任务类别上，常规任务（功能、修复、UI）的接受率最高，而重构和构建等结构性变更成功率较低且处理时间更长。此外，演化分析显示安卓项目的PR解决时间在2025年中期前持续改善，之后再度下降。本研究首次提供了基于证据的AI代理对开源移动端项目影响的刻画，并为评估代理贡献建立了实证基准，有助于设计平台感知的智能开发系统。
原文摘要 · Abstract (English)
AI coding agents are increasingly contributing to software development, yet their impact on mobile development has received little empirical attention. In this paper, we present the first category-level empirical study of agent-generated code in open-source mobile app projects. We analyzed PR acceptance behaviors across mobile platforms, agents, and task categories using 2,901 AI-authored pull requests (PRs) in 193 verified Android and iOS open-source GitHub repositories in the AIDev dataset. We find that Android projects have received 2x more AI-authored PRs and have achieved higher PR acceptance rate (71%) than iOS (63%), with significant agent-level variation on Android. Across task categories, PRs with routine tasks (feature, fix, and ui) achieve the highest acceptance, while structural changes like refactor and build achieve lower success and longer resolution times. Furthermore, our evolution analysis shows improvement in PR resolution time on Android through mid-2025 before it declined again. Our findings offer the first evidence-based characterization of AI agents effects on OSS mobile projects and establish empirical baselines for evaluating agent-generated contributions to design platform aware agentic systems.
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