梳理开发者与AI协作的11种互动模式，助力提升编程效率与信任。
How Developers Interact with AI: A Taxonomy of Human-AI Collaboration in Software Engineering
- 提出11类开发者与AI工具的交互类型，涵盖代码补全、指令驱动等
- 构建系统化分类框架，揭示AI辅助开发中的关键互动机制
- 适合研究人机协作、AI编程工具设计的开发者与学者参考
人工智能（包括大语言模型和生成式AI）正成为软件开发的重要推动力，为开发者提供贯穿整个开发周期的强大工具。尽管软件工程领域已广泛研究AI工具的应用，但开发者与这些AI工具之间的具体交互方式直到近期才开始受到关注。理解并优化此类交互有望提升工作流中的生产力、信任度与效率。本文提出一个开发者与AI工具交互类型的分类体系，识别出十一类不同交互模式，如代码自动补全、命令驱动操作和对话式协助等。基于该分类体系，我们进一步提出研究议程，聚焦于优化AI交互、增强开发者控制力，以及解决信任与可用性挑战。通过建立系统化的研究基础，本文旨在推动更高效、自适应的AI辅助开发工具的发展。
原文摘要 · Abstract (English)
Artificial intelligence (AI), including large language models and generative AI, is emerging as a significant force in software development, offering developers powerful tools that span the entire development lifecycle. Although software engineering research has extensively studied AI tools in software development, the specific types of interactions between developers and these AI-powered tools have only recently begun to receive attention. Understanding and improving these interactions has the potential to enhance productivity, trust, and efficiency in AI-driven workflows. In this paper, we propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types, such as auto-complete code suggestions, command-driven actions, and conversational assistance. Building on this taxonomy, we outline a research agenda focused on optimizing AI interactions, improving developer control, and addressing trust and usability challenges in AI-assisted development. By establishing a structured foundation for studying developer-AI interactions, this paper aims to stimulate research on creating more effective, adaptive AI tools for software development.
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