助力还是干扰？探索与评估主动式AI编程支持的设计与权衡
Assistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Support
AI总结：
本研究通过设计探针Codellaborator评估主动式AI编程支持，发现其提升效率但带来干扰，存在感与上下文可缓解干扰，并强调主动性需适应编程过程。
摘要：
AI编程工具能够实现强大的代码生成，而近期原型尝试通过主动式AI智能体来减少用户的工作量，但这些智能体对编程工作流程的影响仍未得到充分探索。我们引入并评估了Codellaborator，一种基于编辑器活动和任务上下文来启动编程协助的设计探针LLM智能体。我们探索了三种界面变体，以评估日益显著的AI支持之间的权衡：仅提示、主动式智能体，以及带有存在感和上下文的主动式智能体（Codellaborator）。在一项受试者内研究（N=18）中，我们发现，与仅提示范式相比，主动式智能体提高了效率，但也带来了工作流程中断。然而，存在感指示器和交互上下文支持缓解了中断，并提高了用户对AI过程的感知。我们强调了Codellaborator在用户控制、所有权和代码理解方面的权衡，强调需要将主动性适应于编程过程。我们的研究为主动式AI系统的设计探索和评估做出了贡献，提出了对AI集成编程工作流程的设计启示。
英文摘要：
AI programming tools enable powerful code generation, and recent prototypes attempt to reduce user effort with proactive AI agents, but their impact on programming workflows remains unexplored. We introduce and evaluate Codellaborator, a design probe LLM agent that initiates programming assistance based on editor activities and task context. We explored three interface variants to assess trade-offs between increasingly salient AI support: prompt-only, proactive agent, and proactive agent with presence and context (Codellaborator). In a within-subject study (N=18), we find that proactive agents increase efficiency compared to prompt-only paradigm, but also incur workflow disruptions. However, presence indicators and interaction context support alleviated disruptions and improved users' awareness of AI processes. We underscore trade-offs of Codellaborator on user control, ownership, and code understanding, emphasizing the need to adapt proactivity to programming processes. Our research contributes to the design exploration and evaluation of proactive AI systems, presenting design implications on AI-integrated programming workflow.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
