TEASER: Simulation-based CAN Bus Regression Testing for Self-driving Cars Software
Christian Birchler, Cyrill Rohrbach, Hyeongkyun Kim, Alessio Gambi,, Tianhai Liu, Jens Horneber, Timo Kehrer, Sebastiano Panichella

TL;DR
TEASER is a simulation-based tool that generates realistic CAN bus signals for self-driving cars, enabling automated regression testing within CI pipelines to improve safety and reliability.
Contribution
The paper introduces TEASER, a novel tool that automates the generation of realistic CAN signals from simulators for regression testing of self-driving car software.
Findings
TEASER effectively generates realistic CAN signals from simulators.
TEASER integrates into CI pipelines for automated testing.
The approach exposes faults and improves testing automation.
Abstract
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…
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Taxonomy
TopicsReal-time simulation and control systems · Vehicular Ad Hoc Networks (VANETs) · Autonomous Vehicle Technology and Safety
