Towards an Appropriate Level of Reliance on AI: A Preliminary Reliance-Control Framework for AI in Software Engineering - Supplementary Information Package

How software developers interact with Artificial Intelligence (AI)-powered tools, including Large Language Models (LLMs), plays a vital role in how these AI-powered tools in turn impact them. While overreliance on AI may lead to long-term negative consequences (e.g., atrophy of critical thinking skills); underreliance might deprive software developers of potential gains in productivity and quality. Based on twenty-two interviews with software practitioners on using LLMs for software development, we propose a preliminary reliance-control framework where the level of control can be used as a way to identify AI overreliance and underreliance. We also use it to recommend future research to further explore the different control levels supported by the current and emergent LLM-driven tools. Our paper contributes to the emerging discourse on AI overreliance and provides an understanding of the appropriate degree of reliance as essential to developers making the most of these powerful technologies. Our findings can support SE educators in mitigating overreliance towards AI tools by their students.软件开发人员与包括大语言模型（Large Language Model，LLM）在内的人工智能（Artificial Intelligence，AI）辅助工具的交互模式，对这类AI工具反过来作用于开发者的效果有着至关重要的影响。尽管过度依赖AI可能引发长期负面影响（例如批判性思维能力的退化），但依赖不足则会让软件开发人员错失生产力与代码质量提升的潜在收益。本研究通过对22名软件从业者开展的、围绕大语言模型在软件开发中应用情况的访谈，提出了一套初步的依赖-控制框架，该框架以控制程度作为识别AI过度依赖与依赖不足的依据。本研究同时依托该框架，为未来的研究方向提供建议，以进一步探索当前及新兴的大语言模型驱动工具所支持的各类控制层级。本研究为当前围绕AI过度依赖的新兴学术讨论贡献了新的见解，并阐明了适度依赖的重要性——这是软件开发人员充分发挥这类强大技术价值的关键所在。本研究的发现可助力软件工程（Software Engineering，SE）教育者引导学生减轻对AI工具的过度依赖。