Developer Needs and Feasible Features for AI Assistants in IDEs

TL;DR

This study addresses the problem of what developers need from in-IDE AI assistants and which features are realistically feasible. It combines qualitative interviews with 35 developers across Adopters, Churners, and Non-Users and an internal prediction market with 102 practitioners to map user needs to feasible plannable features. The findings show strong alignment for implementation- and context-aware features, but underestimation and lag in proactive behavior and long-term maintenance, highlighting gaps between user demand and near-term feasibility. The work provides structured guidance for prioritizing context-aware, easily implementable features today while reserving capacity for proactive and maintenance capabilities, and demonstrates the value of triangulating user needs with practical feasibility assessments for IDE AI design.

Abstract

Despite the increasing presence of AI assistants in Integrated Development Environments (IDEs), it remains unclear what different groups of developers actually need from these tools and which features are likely to be implemented in practice. To investigate this gap, we conducted a two-phase study. First, we interviewed 35 professional developers from three user groups (Adopters, Churners, and Non-Users) to uncover unmet needs and expectations. Our analysis revealed five key areas of need distinctly distributed across practitioners' groups: Technology Improvement, Interaction, and Customization, as well as Simplifying Skill Building, and Programming Tasks. We then examined the feasibility of addressing selected needs through an internal prediction market involving 102 practitioners. The results demonstrate a strong alignment between the developers' needs and the practitioners' judgment for features focused on implementation and context awareness. However, features related to proactivity and maintenance remain both underestimated and technically unaddressed. Our findings reveal gaps in current AI support and provide practical directions for developing more effective and sustainable in-IDE AI systems

Figures (2)

• Figure 1: The Categorization of Users' Needs in HAX Within IDEs With Descriptions
• Figure 2: Comparative Analysis of Thematic Group Mentions Across User Types.