Unveiling Code Clone Patterns in Open Source VR Software: An Empirical Study

2025-01-13

AI Summary

Code cloning in open-source virtual reality (VR) software remains underexplored, despite its potential impact on software quality and security. Method: We propose a cross-modal clone detection framework integrating NiCad with large language models (LLMs) to jointly identify clones across source code and serialized assets (e.g., Unity Prefabs and Scenes), applied to 345 free VR projects. Contribution/Results: Our study is the first to systematically quantify clone prevalence in VR software, revealing significantly higher cross-modal clone density than in conventional software. We identify high-frequency clone hotspots and empirically validate their implications for maintenance and security through seven VR-specific research questions. The findings provide evidence-based insights and actionable guidelines for VR development practice, establishing the first empirical foundation for clone analysis in the VR domain.

Technology Category

• Computer Vision: Multi-modal Vision
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Sustainability and climate impact of web technologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Code cloning is frequently observed in software development, often leading to a variety of maintenance and security issues. While substantial research has been conducted on code cloning in traditional software, to the best of my knowledge, there is a lack of studies on cloning in VR software that consider its unique nature, particularly the presence of numerous serialized files in conjunction with the source code. In this paper, we conduct the first large-scale quantitative empirical analysis of software clones in 345 open-source VR projects, using the NiCad detector for source code clone detection and large language models (LLMs) for identifying serialized file clones. Our study leads to a number of insights into cloning phenomena in VR software, guided by seven carefully formulated research questions. These findings, along with their implications, are anticipated to provide useful guidance for both researchers and software developers within the VR field.

Problem

• Virtual Reality Software
• Code Clone
• Software Security

Innovation

• Virtual Reality
• Code Cloning
• Software Analysis