一句话：提出 Helion，用 n-gram 语言模型学习智能家居例程规律，生成自然的家庭自动化测试场景。
智能家居安全与安全性测试系统要在实践中可用，必须用能代表真实家庭自动化使用的场景来测试。本文（demo 论文）介绍 Helion 的技术细节与使用方式：它利用 n-gram 语言模型学习用户编写的智能家居例程（routine）中的规律，并据此预测自然的家庭自动化场景，即反映真实使用的事件序列。作者进一步将 Helion 与流行的 Home Assistant 平台集成，构建了 HelionHA 平台，使这些场景能够作为测试用例，在真实和虚拟智能家居设备上端到端执行与探索，从而为智能家居测试提供更贴近实际的输入。
Prior work has developed numerous systems that test the security and safety of smart homes. For these systems to be applicable in practice, it is necessary to test them with realistic scenarios that represent the use of the smart home, i.e., home automation, in the wild. This demo paper presents the technical details and usage of Helion, a system that uses n-gram language modeling to learn the regularities in user-driven programs, i.e., routines developed for the smart home, and predicts natural scenarios of home automation, i.e., event sequences that reflect realistic home automation usage. We demonstrate the HelionHA platform, developed by integrating Helion with the popular Home Assistant smart home platform. HelionHA allows an end-to-end exploration of Helion’s scenarios by executing them as test cases with real and virtual smart home devices.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{MandalMKMPN23,
title = {Helion: Enabling Natural Testing of Smart Homes},
author = {Prianka Mandal and Sunil Manandhar and Kaushal Kafle and Kevin Moran and Denys Poshyvanyk and Adwait Nadkarni},
booktitle = {FSE},
year = {2023},
doi = {10.1145/3611643.3613095}
}