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

TL;DR

This paper explores how software developers interact with AI tools like LLMs, proposing a reliance-control framework to balance overreliance and underreliance for optimal productivity and skill retention.

Contribution

It introduces a preliminary reliance-control framework based on interviews, guiding future research and responsible AI use in software engineering.

Findings

Framework identifies AI overreliance and underreliance levels.

Provides recommendations for balancing AI reliance.

Supports responsible AI integration in development practices.

Abstract

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 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 developers 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 help practitioners, educators, and policymakers promote responsible and effective use of AI tools.