标题： 测量早期2025年人工智能对经验丰富的开源开发者生产力的影响 显示英文标题

标题： Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

摘要： 尽管广泛采用，人工智能工具对实际软件开发的影响仍研究不足。我们进行了一项随机对照试验（RCT），以了解2025年2月至6月前沿的人工智能工具如何影响有经验的开源开发人员的生产力。16名具有中等人工智能经验的开发人员在他们平均有5年之前经验的成熟项目中完成了246项任务。每项任务都被随机分配以允许或不允许使用2025年初的人工智能工具。当允许使用人工智能工具时，开发人员主要使用Cursor Pro，这是一种流行的代码编辑器，以及Claude 3.5/3.7 Sonnet。在开始任务之前，开发人员预测允许使用人工智能将减少完成时间24%。完成研究后，开发人员估计允许使用人工智能将减少完成时间20%。令人惊讶的是，我们发现允许使用人工智能实际上增加了完成时间19%——人工智能工具使开发人员变慢了。这种减速也与经济学专家（39%更短）和机器学习专家（38%更短）的预测相矛盾。为了理解这一结果，我们收集并评估了20个可能对观察到的减速效应有贡献的设置属性的证据——例如，项目的规模和质量标准，或者开发人员之前使用人工智能工具的经验。虽然无法完全排除实验人工制品的影响，但减速效应在我们的分析中的稳健性表明，这不太可能是我们的实验设计的主要功能。 显示英文摘要

摘要： Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February-June 2025 frontier affect the productivity of experienced open-source developers. 16 developers with moderate AI experience complete 246 tasks in mature projects on which they have an average of 5 years of prior experience. Each task is randomly assigned to allow or disallow usage of early 2025 AI tools. When AI tools are allowed, developers primarily use Cursor Pro, a popular code editor, and Claude 3.5/3.7 Sonnet. Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%--AI tooling slowed developers down. This slowdown also contradicts predictions from experts in economics (39% shorter) and ML (38% shorter). To understand this result, we collect and evaluate evidence for 20 properties of our setting that a priori could contribute to the observed slowdown effect--for example, the size and quality standards of projects, or prior developer experience with AI tooling. Although the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.