Stefan Klikovits, Alessio Gambi, Deepak Dhungana, Rick Rabiser
Technical Track
 | Haus der Universität, Schlösslistrasse 5, 3008 Bern, Switzerland |
 | 8 February 2024, 11:00 CET |
 | Rick Rabiser |
 | Florian Jost |
 | https://dl.acm.org/doi/10.1145/3634713.3634720 |
Extensive testing of Automated Driving Systems (ADS), such as Advanced Driver Assistance Systems and Autonomous Vehicles, is commonly conducted using simulators programmed to implement various driving scenarios, a technique known as scenario-based testing. ADS scenario-based testing using simulations is challenging because it requires identifying scenarios that can effectively test ADS functionalities while ensuring that driving simulators’ features match the driving scenarios’ requirements. This short paper discusses the main challenges of systematically conducting simulation-based testing and proposes leveraging Software Product Line techniques to address them. Specifically, we argue that variability models can be used to support testers in generating test scenarios by effectively capturing and relating the variability in driving simulators, testing scenarios, and ADS implementations. We conclude by outlining an agenda for future research in this important area.