PredART: Towards Automatic Oracle Prediction of Object Placements in Augmented Reality Testing

Abstract

While the emerging Augmented Reality (AR) technique allows a lot of new application opportunities, from education and communication to gaming, current augmented apps often have complaints about their usability and/or user experience due to placement errors of virtual objects. Therefore, identifying noticeable placement errors is an important goal in the testing of AR apps. However, placement errors can only be perceived by human beings and may need to be confirmed by multiple users, making automatic testing very challenging. In this paper, we propose PredART, a novel approach to predict human ratings of virtual object placements that can be used as test oracles in automated AR testing. PredART is based on automatic screenshot sampling, crowd sourcing, and a hybrid neural network for image regression. The evaluation on a test set of 480 screenshots shows that our approach can achieve an accuracy of 85.0% and a mean absolute error, mean squared error, and root mean squared error of 0.047, 0.008, and 0.091, respectively.