  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
