AI in Software Engineering: Perceived Roles and Their Impact on Adoption

TL;DR

Problem: understanding how developers' attribution of AI roles influences adoption of AI-powered Development Tools in software engineering. Approach: mixed-methods with 38 qualitative interviews and a 102-participant online survey, using factor analysis and correlations to link role attributions to acceptance measures. Findings: two mental-model dimensions (inanimate tool vs human-like teammate) and two role clusters (Expert Roles, Support Roles); both clusters show positive associations with $PU$ and $PEU$, and experience with coding does not strongly modulate these relationships. Significance: findings inform adaptive design, personalized onboarding, and framing of AI tools to align with diverse developer expectations and enhance human-AI collaboration in AI4SE.

Abstract

This paper investigates how developers conceptualize AI-powered Development Tools and how these role attributions influence technology acceptance. Through qualitative analysis of 38 interviews and a quantitative survey with 102 participants, we identify two primary Mental Models: AI as an inanimate tool and AI as a human-like teammate. Factor analysis further groups AI roles into Support Roles (e.g., assistant, reference guide) and Expert Roles (e.g., advisor, problem solver). We find that assigning multiple roles to AI correlates positively with Perceived Usefulness and Perceived Ease of Use, indicating that diverse conceptualizations enhance AI adoption. These insights suggest that AI4SE tools should accommodate varying user expectations through adaptive design strategies that align with different Mental Models.

Figures (1)

• Figure 1: Hierarchical Clustering of the roles that developers assign to AI