How Developers Interact with AI: A Taxonomy of Human-AI Collaboration in Software Engineering
This work addresses the need for improved productivity, trust, and efficiency in AI-driven software development workflows, but it is incremental as it builds on existing studies to categorize interactions rather than introducing new methods.
The paper tackles the problem of understanding interactions between developers and AI tools in software engineering by proposing a taxonomy of eleven distinct interaction types, such as auto-complete code suggestions and conversational assistance, to establish a structured foundation for research.
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.