Search-based software testing (SBT) is an effective and efficient approach for testing automated driving systems (ADS). However, testing pipelines for ADS testing are particularly challenging as they involve integrating complex driving simulation platforms and establishing communication protocols and APIs with the desired search algorithm. This complexity prevents a wide adoption of SBT and thorough empirical comparative experiments with different simulators and search approaches. We present OpenSBT, an open-source, modular and extensible framework to facilitate the SBT of ADS. With OpenSBT, it is possible to integrate simulators with an embedded system under test, search algorithms and fitness functions for testing. We describe the architecture and show the usage of our framework by applying different search algorithms for testing Automated Emergency Braking Systems in CARLA as well in the high-fidelity Prescan simulator in collaboration with our industrial partner DENSO. OpenSBT is available at https://git.fortiss.org/opensbt. A demo video is provided here: https://youtu.be/6csl\_UAOD\_4.

翻译：搜索式软件测试（SBT）是一种有效且高效的自动驾驶系统（ADS）测试方法。然而，ADS测试管道因需集成复杂的驾驶仿真平台、建立通信协议及所需搜索算法的应用程序接口而极具挑战性。该复杂性阻碍了SBT的广泛采用以及不同仿真器与搜索方法间的严格实证比较实验。我们提出OpenSBT——一个开源、模块化且可扩展的框架，旨在促进ADS的SBT。借助OpenSBT，可集成带嵌入式被测系统的仿真器、搜索算法及用于测试的适应度函数。我们描述了该框架的架构，并通过在CARLA中应用不同搜索算法测试自动紧急制动系统，以及联合工业合作伙伴DENSO在高保真Prescan仿真器中的应用演示了框架的使用。OpenSBT代码见https://git.fortiss.org/opensbt，演示视频见https://youtu.be/6csl_UAOD_4。