一句话：提出 PathFinder，用好奇心强化学习自动探索 HMI 软件结构以提升测试覆盖。
在缺乏软件逻辑先验知识时，高效且充分地发现 HMI 软件底层结构以支持测试仍然困难，主要挑战在于 HMI 软件复杂且现有方法覆盖率方差大。本文提出 PathFinder，一个自动 HMI 软件探索框架。它采用基于好奇心的强化学习，选择能发现更多未知状态的动作，并在探索过程中逐步构建导航模型，以进一步提高状态覆盖率。作者在仿真环境和真实 HMI 软件测试环境（包含汽车仪表盘完整工具链）上进行实验，结果表明其探索覆盖率优于当前工业标准的手工测试与模糊测试方法。
Discovering the underlying structure of HMI software efficiently and sufficiently for the purpose of testing without any prior knowledge on the software logic remains a difficult problem. The key challenge lies in the complexity of the HMI software and the high variance in the coverage of current methods. In this paper, we introduce the PathFinder, an effective and automatic HMI software exploration framework. PathFinder adopts a curiosity-based reinforcement learning framework to choose actions that lead to the discovery of more unknown states. Additionally, PathFinder progressively builds a navigation model during the exploration to further improve state coverage. We have conducted experiments on both simulations and real-world HMI software testing environment, which comprise a full tool chain of automobile dashboard instrument cluster. The exploration coverage outperforms manual and fuzzing methods which are the current industrial standards.
DOI 原文 ·
@inproceedings{paperbot6535,
title = {Automatic HMI Structure Exploration Via Curiosity-Based Reinforcement Learning},
author = {Yushi Cao and Yan Zheng and Shang-Wei Lin and Yang Liu and Yon Shin Teo and Yuxuan Toh and Vinay Vishnumurthy Adiga},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678703}
}