PredART: Towards Automatic Oracle Prediction of Object Placements in Augmented Reality Testing
PredART: Towards Automatic Oracle Prediction of Object Placements in Augmented Reality Testing
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DOI：
10.1145/3551349.3561160
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Tahmid Rafi;Xueling Zhang;Xiaoyin Wang
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文献类型：
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作者：
Tahmid Rafi;Xueling Zhang;Xiaoyin Wang
虽然新兴的增强现实（AR）技术允许从教育和通信到游戏的许多新的应用机会，但是由于虚拟对象的放置错误，当前的增强应用经常对其可用性和/或用户体验有抱怨。因此，识别明显的放置错误是AR应用程序测试的一个重要目标。然而，放置错误只能由人类感知，并且可能需要多个用户确认，这使得自动测试非常具有挑战性。在本文中，我们提出了PredART，一种新的方法来预测虚拟对象放置的人类评级，可用作自动AR测试中的测试神谕。PredART基于自动屏幕截图采样、众包和用于图像回归的混合神经网络。在480张截图的测试集上的评估表明，我们的方法可以达到85.0%的准确率，平均绝对误差，均方误差和均方根误差分别为0.047，0.008和0.091。
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.