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P2 PredART: Towards Automatic Oracle Prediction of Object Placements in Augmented Reality Testing

PredART：面向增强现实测试中对象放置的自动测试预言预测
ASE Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering2022匹配度 1/10 · 该论文研究AR应用测试中虚拟对象放置的测试预言预测，属于软件测试与计算机视觉交叉方向，不涉及静态分析、程序分析、浮点程序分析、贝叶斯程序分析，也不涉及LLM/Agent在PL或验证中的应用。与用户研究画像的核心方向基本无关。引用 16 inbox
Tahmid Rafi, Xueling Zhang, Xiaoyin Wang
测试、调试与修复
crossref: 2026-09-12

AI 速览

一句话：提出 PredART，通过截图采样、众包与混合神经网络预测虚拟对象放置的人类评分，作为 AR 自动化测试的测试预言。

增强现实应用常因虚拟对象放置错误而影响可用性与用户体验，但放置错误只能由人类感知且需多人确认，导致自动测试困难。本文提出 PredART，一种预测人类对虚拟对象放置评分的自动测试预言方法。PredART 采用自动截图采样、众包标注和混合神经网络进行图像回归，从而预测放置质量。在 480 张截图的测试集上，方法取得 85.0% 的准确率，MAE、MSE 和 RMSE 分别为 0.047、0.008 和 0.091，表明其可作为 AR 自动化测试的有效预言。

问题
AR 应用中虚拟对象放置错误难以自动检测，因其感知与确认依赖人类主观判断，缺乏可用测试预言。
方法
提出 PredART，结合自动截图采样、众包和混合神经网络图像回归，预测人类对放置质量的评分。
评估
在 480 张截图的测试集上评估，准确率为 85.0%，MAE、MSE、RMSE 分别为 0.047、0.008、0.091。
原文摘要（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.

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BibTeX 
@inproceedings{paperbot7181,
  title = {PredART: Towards Automatic Oracle Prediction of Object Placements in Augmented Reality Testing},
  author = {Tahmid Rafi and Xueling Zhang and Xiaoyin Wang},
  booktitle = {ASE},
  year = {2022},
  doi = {10.1145/3551349.3561160}
}