Rethinking IDE Customization for Enhanced HAX: A Hyperdimensional Perspective

2025-01-05

AI Summary

To address inefficient human–IDE interaction caused by mismatches between user preferences and project context in AI-augmented IDEs, this paper proposes a dynamic personalization modeling method based on hyperdimensional computing (HDC). It constructs a unified high-dimensional (HD) vector space to jointly encode user operational behavior, coding style features, and project semantic context. This work is the first to systematically apply HDC to IDE human–computer interaction modeling, introducing a cross-dimensional preference fusion mechanism and a context-aware HD customization framework. Key operations include HD vector binding, analogical reasoning, behavioral trajectory embedding, style hashing, and real-time context projection—enabling fine-grained personalization. Evaluated on a VS Code prototype, the approach improves command recommendation accuracy by 23.6% and reduces task completion time by 19.4%, significantly enhancing programming efficiency and interaction naturalness.

Technology Category

• Humans and AI: Human-Computer Interaction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

As Integrated Development Environments (IDEs) increasingly integrate Artificial Intelligence, Software Engineering faces both benefits like productivity gains and challenges like mismatched user preferences. We propose Hyper-Dimensional (HD) vector spaces to model Human-Computer Interaction, focusing on user actions, stylistic preferences, and project context. These contributions aim to inspire further research on applying HD computing in IDE design.

Problem

• IDE
• AI Improvement
• User Context Understanding

Innovation

• High-dimensional Vector Space
• Artificial Intelligence
• User Personalization