Towards Automatic Oracle Prediction for AR Testing: Assessing Virtual Object Placement Quality under Real-World Scenes

Xiaoyi Yang, Yuxing Wang, Tahmid Rafi, Dongfang Liu, Xiaoyin Wang, Xueling Zhang

Abstract: Augmented Reality (AR) technology opens up exciting possibilities in various fields, such as education, work guidance, shopping, communication, and gaming. However, users often encounter usability and user experience issues in current AR apps, often due to the imprecise placement of virtual objects. Detecting these inaccuracies is crucial for AR app testing, but automating the process is challenging due to its reliance on human perception and validation. This paper introduces VOPA (Virtual Object Placement Assessment), a novel approach that automatically identifies imprecise virtual object placements in real-world AR apps. VOPA involves instrumenting real-world AR apps to collect screenshots representing various object placement scenarios and their corresponding metadata under real-world scenes. The collected data are then labeled through crowdsourcing and used to train a hybrid neural network that identifies object placement errors. VOPA aims to enhance AR app testing by automating the assessment of virtual object placement quality and detecting imprecise instances. In our evaluation of a test set of 304 screenshots, VOPA achieved an accuracy of 99.34%, precision of 96.92% and recall of 100%. Furthermore, VOPA successfully identified 38 real-world object placement errors, including instances where objects were hovering between two surfaces or appearing embedded in the wall.