Exploiting deep reinforcement learning and metamorphic testing to automatically test virtual reality applications

Despite the rapid growth and popularization of virtual reality (VR) applications, which have enabled new concepts for handling and solving existing problems through VR in various domains, practices related to software engineering have not kept up with this growth. Recent studies indicate that one of the topics that is still little explored in this area is software testing, as VR applications can be built for practically any type of purpose, making it difficult to generalize knowledge to be applied. In this paper, we present an approach that combines metamorphic testing, agent-based testing and machine learning to test VR applications, focusing on finding collision and camera-related faults. Our approach proposes the use of metamorphic relations to detect faults in collision and camera components in VR applications, as well as the use of intelligent agents for the automatic generation of test data. To evaluate the proposed approach, we conducted an experimental study on four VR applications, and the results showed an accuracy$$ accuracy $$ of the solution ranging from 93% to 69%, depending on the complexity of the application tested. We also discussed the feasibility of extending the approach to identify other types of faults in VR applications. In conclusion, we discussed important trends and opportunities that can benefit both academics and practitioners. This paper addresses challenges in software testing for virtual reality (VR) applications. We introduce a novel approach combining metamorphic testing, agent-based testing, and machine learning to detect collision and camera-related faults in VR applications. The proposed methodology involves metamorphic relations and intelligent agents for test data generation. An experimental study on four VR applications demonstrates the approach's effectiveness, yielding accuracy ranging from 93% to 69%. We discuss the potential extension of the approach and its implications for researchers and practitioners.image