@inproceedings{6642a9d11d7e452eb7cd6c41e849b47d,
title = "A Natural Language Guided Adaptive Model-based Testing Tool for Autonomous Driving",
abstract = "Testing Autonomous Driving Systems (ADS) is critical to ensure their safety and reliability in dynamic and unpredictable real-world driving environments. In the literature, many scenario-based ADS testing solutions have been proposed to generate safety-critical driving scenarios. Along a similar research line, in this paper, we present a tool, named LiveTCM, which has a web-based model editor for specifying and executing Test Case Specifications (TCS). LiveTCM also has an extensible engine for enabling generation of TCS via real-time communication with the ADS (i.e., the system under test) situated in a simulated ADS driving environment. Videos illustrating the capabilities of LiveTCM can be found at: https://github.com/WSE-Lab/LiveTCM.",
keywords = "ADS Testing, Model Execution, Model-based Testing, Natural Language",
author = "Man Zhang and Peiru Li and Yize Shi and Tao Yue",
note = "Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s).; 16th International Conference on Internetware, Internetware 2025 ; Conference date: 20-06-2025 Through 22-06-2025",
year = "2025",
month = oct,
day = "27",
doi = "10.1145/3755881.3755932",
language = "英语",
series = "16th International Conference on Internetware, Internetware 2025 - Proceedings",
publisher = "Association for Computing Machinery, Inc",
pages = "537--540",
editor = "Hong Mei and Jian Lv and Zhi Jin and Xuandong Li and Thomas Zimmermann and Ge Li and Lei Bu and Xin Xia",
booktitle = "16th International Conference on Internetware, Internetware 2025 - Proceedings",
}