VRExplorer: A Model-based Approach for Semi-Automated Testing of Virtual Reality Scenes

2026-07-11

AI Summary

This study addresses the challenge of ensuring software quality in virtual reality (VR) applications, which exhibit diverse interactions and complex 3D environments that hinder the effectiveness of existing testing approaches. To overcome this limitation, the authors propose a model-based semi-automated testing method centered on a novel Entity–Action–Task (EAT) modeling framework. This framework integrates Unity NavMesh path planning with probabilistic finite state machines (PFSMs) to guide autonomous agents in systematically exploring VR environments and executing high-coverage test scenarios. Evaluated on eleven real-world VR projects, the approach significantly outperforms the state-of-the-art tool VRGuide, achieving a 122.8% improvement in ELOC coverage and a 52.8% increase in method coverage, while successfully uncovering three previously unknown defects.

Abstract

With the proliferation of Virtual Reality (VR) markets, VR applications are rapidly expanding in scale and complexity, thereby driving an urgent need for assuring VR software quality. Different from traditional mobile applications and computer software, VR testing faces unique challenges due to diverse interactions with virtual objects, complex 3D virtual environments, and intricate sequences to complete tasks. All of these emerging challenges hinder existing VR testing tools from effectively and systematically testing VR applications. In this paper, we present VRExplorer, a novel model-based testing tool to effectively interact with diverse virtual objects and explore complex VR scenes. Particularly, we design the Entity, Action, and Task (EAT) framework for modeling diverse VR interactions in a generic way. Built upon the EAT framework, we then present the VRExplorer agent, which can achieve effective scene exploration by incorporating meticulously designed path-finding algorithms into Unity's NavMesh. Moreover, the VRExplorer agent can also systematically execute interaction decisions on top of the Probabilistic Finite State Machine (PFSM). Experimental evaluation on 11 representative VR projects shows that VRExplorer consistently outperforms the state-of-the-art (SOTA) approach VRGuide by achieving significantly higher coverage and better efficiency. Specifically, VRExplorer yields up to 122.8% and 52.8% improvements over VRGuide in terms of executable lines of code (ELOC) coverage and method (function) coverage, respectively. Furthermore, ablation results also verify the essential contributions of each designed module. More importantly, our VRExplorer has successfully detected two functional bugs and one non-functional bug from real-world projects.

Problem

• Virtual Reality
• Software Testing
• 3D Virtual Environments
• Interaction Complexity
• Test Coverage

Innovation

• Model-based testing
• Virtual Reality
• EAT framework
• Path-finding algorithm
• Probabilistic Finite State Machine