Fast-Forward Reality: Authoring Error-Free Context-Aware Policies with Real-Time Unit Tests in Extended Reality
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910364755558400 |
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| author | Qian, Xun Wang, Tianyi Xu, Xuhai Jonker, Tanya R Todi, Kashyap |
| author_facet | Qian, Xun Wang, Tianyi Xu, Xuhai Jonker, Tanya R Todi, Kashyap |
| contents | Advances in ubiquitous computing have enabled end-user authoring of context-aware policies (CAPs) that control smart devices based on specific contexts of the user and environment. However, authoring CAPs accurately and avoiding run-time errors is challenging for end-users as it is difficult to foresee CAP behaviors under complex real-world conditions. We propose Fast-Forward Reality, an Extended Reality (XR) based authoring workflow that enables end-users to iteratively author and refine CAPs by validating their behaviors via simulated unit test cases. We develop a computational approach to automatically generate test cases based on the authored CAP and the user's context history. Our system delivers each test case with immersive visualizations in XR, facilitating users to verify the CAP behavior and identify necessary refinements. We evaluated Fast-Forward Reality in a user study (N=12). Our authoring and validation process improved the accuracy of CAPs and the users provided positive feedback on the system usability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07997 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast-Forward Reality: Authoring Error-Free Context-Aware Policies with Real-Time Unit Tests in Extended Reality Qian, Xun Wang, Tianyi Xu, Xuhai Jonker, Tanya R Todi, Kashyap Human-Computer Interaction H.5.2 Advances in ubiquitous computing have enabled end-user authoring of context-aware policies (CAPs) that control smart devices based on specific contexts of the user and environment. However, authoring CAPs accurately and avoiding run-time errors is challenging for end-users as it is difficult to foresee CAP behaviors under complex real-world conditions. We propose Fast-Forward Reality, an Extended Reality (XR) based authoring workflow that enables end-users to iteratively author and refine CAPs by validating their behaviors via simulated unit test cases. We develop a computational approach to automatically generate test cases based on the authored CAP and the user's context history. Our system delivers each test case with immersive visualizations in XR, facilitating users to verify the CAP behavior and identify necessary refinements. We evaluated Fast-Forward Reality in a user study (N=12). Our authoring and validation process improved the accuracy of CAPs and the users provided positive feedback on the system usability. |
| title | Fast-Forward Reality: Authoring Error-Free Context-Aware Policies with Real-Time Unit Tests in Extended Reality |
| topic | Human-Computer Interaction H.5.2 |
| url | https://arxiv.org/abs/2403.07997 |