Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming
This work addresses inefficiencies in human-AI collaboration for programmers using code-recommendation tools, but it is incremental as it focuses on modeling interactions rather than proposing a new system.
The study tackled the problem of understanding programmer interactions with AI code-recommendation systems like GitHub Copilot to improve productivity, resulting in the development of CUPS, a taxonomy that revealed inefficiencies and time costs in a study of 21 programmers.
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.