Robust Testing for Cyber-Physical Systems using Reinforcement Learning
Robust Testing for Cyber-Physical Systems using Reinforcement Learning
复制标题
DOI：
10.1145/3610579.3611087
复制
发表时间：
2023-09
期刊：
影响因子：
--
通讯作者：
Xin Qin;Nikos Aréchiga;Jyotirmoy V. Deshmukh;Andrew Best
中科院分区：
文献类型：
--
作者：
Xin Qin;Nikos Aréchiga;Jyotirmoy V. Deshmukh;Andrew Best
在本文中，我们提出了一个在不确定环境中运行的网络物理系统（CPS）的测试框架。测试此类CPS应用程序需要仔细定义环境，以包括CPS可能遇到的所有可能的实际操作场景。同时，测试过程希望确定在其中被测系统（SUT）违反其规范的操作场景。我们提出了一种新的测试方法，基于使用深度强化学习对给定SUT进行鲁棒性测试。在健壮的测试框架中，测试生成工具可以提供有意义且具有挑战性的测试，即使SUT只有很小的变化。这种方法在增量设计方法中非常有价值，在这种方法中，对设计的小更改不需要从头开始生成昂贵的测试。我们在一个逼真的自动驾驶模拟器中实现了三个自动驾驶示例系统，证明了我们的方法的有效性。
In this paper, we propose a testing framework for cyber-physical systems (CPS) that operate in uncertain environments. Testing such CPS applications requires carefully defining the environment to include all possible realistic operating scenarios that the CPS may encounter. Simultaneously, the process of testing hopes to identify operating scenarios in which the system-under-test (SUT) violates its specifications. We present a novel approach of testing based on the use of deep reinforcement learning for robust testing of a given SUT. In a robust testing framework, the test generation tool can provide meaningful and challenging tests even when there are small changes to the SUT. Such a method can be quite valuable in incremental design methods where small changes to the design does not necessitate expensive test generation from scratch. We demonstrate the efficacy of our method on three example systems in autonomous driving implemented within a photo-realistic autonomous driving simulator.