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.

翻译：搜索式软件测试（SBT）是测试自动驾驶系统（ADS）的一种高效方法。然而，对ADS进行测试的流程尤为复杂，其原因在于需要整合复杂的驾驶仿真平台，并建立与目标搜索算法之间的通信协议和应用程序接口（API）。这一复杂性阻碍了SBT的广泛采用，以及针对不同仿真器和搜索方法开展深入的实证对比实验。本文提出OpenSBT，一个开源、模块化且可扩展的框架，旨在推动ADS的SBT实现。借助OpenSBT，用户能够将仿真器、待测嵌入式系统、搜索算法及适应度函数集成至测试流程中。我们描述了该框架的架构，并通过在CARLA仿真环境以及与工业合作伙伴DENSO合作的高保真Prescan仿真器中，应用不同搜索算法对自动紧急制动系统进行测试的案例，展示了其使用方法。OpenSBT代码开源，可通过https://git.fortiss.org/opensbt获取。