UltraInstinctVR: A Model-Based Tool for Automated System Testing of VR Applications

Abstract

Virtual Reality (VR) applications are increasingly used in domains such as surgical training and industrial marketing, yet their longterm adoption remains limited by the lack of effective, systematic, and reproducible testing approaches tailored to VR. To address this gap, we present UltraInstinctVR, a novel tool that automates the generation and execution of concrete system-level tests for VR applications, helping developers efficiently validate interactions and behaviors. We evaluated UltraInstinctVR against state-of-the-art automated VR testing tools across 10 open-source VR applications, where it outperformed existing approaches in detecting unique failures and uncovering real-world bugs. In this paper, we further extended this evaluation through an empirical user study with ten VR developers. Results highlight strong dependability and efficiency while identifying opportunities for improvement in other user experience dimensions. Overall, these findings position UltraInstinctVR as a promising step toward more practical, effective, and scalable VR testing.

Gerry Longfils

PhD Student

Xavier Devroey

Professor of Software Engineering

My research goal is to to ease software testing by exploring new paths to achieve a high level of automation for test case design, generation, selection, and prioritization. My main research interests include search-based and model-based software testing, test suite augmentation, DevOps, and variability-intensive systems.