Software systems for safety-critical systems like self-driving cars (SDCs) need to be tested rigorously. Especially electronic control units (ECUs) of SDCs should be tested with realistic input data. In this context, a communication protocol called Controller Area Network (CAN) is typically used to transfer sensor data to the SDC control units. A challenge for SDC maintainers and testers is the need to manually define the CAN inputs that realistically represent the state of the SDC in the real world. To address this challenge, we developed TEASER, which is a tool that generates realistic CAN signals for SDCs obtained from sensors from state-of-the-art car simulators. We evaluated TEASER based on its integration capability into a DevOps pipeline of aicas GmbH, a company in the automotive sector. Concretely, we integrated TEASER in a Continous Integration (CI) pipeline configured with Jenkins. The pipeline executes the test cases in simulation environments and sends the sensor data over the CAN bus to a physical CAN device, which is the test subject. Our evaluation shows the ability of TEASER to generate and execute CI test cases that expose simulation-based faults (using regression strategies); the tool produces CAN inputs that realistically represent the state of the SDC in the real world. This result is of critical importance for increasing automation and effectiveness of simulation-based CAN bus regression testing for SDC software. Tool: https://doi.org/10.5281/zenodo.7964890 GitHub: https://github.com/christianbirchler-org/sdc-scissor/releases/tag/v2.2.0-rc.1 Documentation: https://sdc-scissor.readthedocs.io

翻译：安全关键系统（如自动驾驶汽车）的软件系统需要经过严格测试。特别是自动驾驶汽车中的电子控制单元（ECU），应使用真实输入数据进行测试。在此背景下，通常采用名为控制器局域网络（CAN）的通信协议将传感器数据传输至自动驾驶汽车控制单元。自动驾驶汽车维护人员和测试人员面临的一项挑战是需手动定义能真实反映车辆现实状态的CAN输入。为应对这一挑战，我们开发了TEASER——一种从先进车载模拟器中获取传感器数据、为自动驾驶汽车生成真实CAN信号的工具。我们基于该工具在汽车领域企业aicas GmbH的DevOps流水线中的集成能力进行了评估。具体而言，我们将TEASER集成至使用Jenkins配置的持续集成（CI）流水线中。该流水线在仿真环境中执行测试用例，并通过CAN总线将传感器数据发送至物理CAN设备（即被测对象）。评估结果表明，TEASER能够生成并执行可暴露基于模拟的故障的CI测试用例（采用回归策略）；该工具产生的CAN输入能真实反映自动驾驶汽车在现实世界中的状态。这一结果对于提升自动驾驶汽车软件基于模拟的CAN总线回归测试的自动化程度与有效性具有关键意义。工具：https://doi.org/10.5281/zenodo.7964890 GitHub：https://github.com/christianbirchler-org/sdc-scissor/releases/tag/v2.2.0-rc.1 文档：https://sdc-scissor.readthedocs.io