In-IDE Human-AI Experience in the Era of Large Language Models; A Literature Review

TL;DR

This literature review examines how AI integration in IDEs affects programmer interactions, productivity, and code quality, highlighting current research trends, challenges, and future opportunities in Human-AI collaborative software development.

Contribution

The paper synthesizes existing research on in-IDE Human-AI Experience, identifying key themes and proposing future research directions in UI design, trust, and readability improvements.

Findings

Identified three main research areas: Design, Impact, and Quality of Interaction.

Highlighted challenges in trust-building and interface design for AI tools.

Outlined opportunities for enhancing programmer-AI collaboration in IDEs.

Abstract

Integrated Development Environments (IDEs) have become central to modern software development, especially with the integration of Artificial Intelligence (AI) to enhance programming efficiency and decision-making. The study of in-IDE Human-AI Experience is critical in understanding how these AI tools are transforming the software development process, impacting programmer productivity, and influencing code quality. We conducted a literature review to study the current state of in-IDE Human-AI Experience research, bridging a gap in understanding the nuanced interactions between programmers and AI assistants within IDEs. By analyzing 36 selected papers, our study illustrates three primary research branches: Design, Impact, and Quality of Interaction. The trends, challenges, and opportunities identified in this paper emphasize the evolving landscape of software development and inform future directions for research and development in this dynamic field. Specifically, we invite the community to investigate three aspects of these interactions: designing task-specific user interface, building trust, and improving readability.