论文标题
在行之间阅读：在AI辅助编程中对用户行为和成本进行建模
Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming
论文作者
论文摘要
诸如Copilot和CodeWhisperer之类的代码推荐系统有可能通过建议和自动完成代码来提高程序员的生产率。但是，为了充分发挥其潜力，我们必须了解程序员如何与这些系统进行互动并确定改善这种交互的方法。为了寻求有关人类与代码建议系统合作的见解，我们研究了GitHub Copilot，这是数百万程序员每天使用的代码启用系统。我们开发了杯子，这是一种与副驾驶互动时的共同程序员活动的分类法。我们对21名程序员的研究完成了编码任务并回顾性地标记了他们的杯子会议，这表明杯子可以帮助我们了解程序员如何与代码责备系统互动，从而揭示了效率低下和时间成本。我们的见解揭示了程序员如何与Copilot互动并激励新的界面设计和指标。
Code-recommendation systems, such as Copilot and CodeWhisperer, have the potential to improve programmer productivity by suggesting and auto-completing code. However, to fully realize their potential, we must understand how programmers interact with these systems and identify ways to improve that interaction. To seek insights about human-AI collaboration with code recommendations systems, we studied GitHub Copilot, a code-recommendation system used by millions of programmers daily. We developed CUPS, a taxonomy of common programmer activities when interacting with Copilot. Our study of 21 programmers, who completed coding tasks and retrospectively labeled their sessions with CUPS, showed that CUPS can help us understand how programmers interact with code-recommendation systems, revealing inefficiencies and time costs. Our insights reveal how programmers interact with Copilot and motivate new interface designs and metrics.
