Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor

Abstract

Simulation platforms facilitate the continuous development of complex systems such as self-driving cars (SDCs). However, previous results on testing SDCs using simulations have shown that most of the automatically generated tests do not strongly contribute to establishing confidence in the quality and reliability of the SDC. Therefore, those tests can be characterized as “uninformative”, and running them generally means wasting precious computational resources. We address this issue with SDC-Scissor, a framework that leverages Machine Learning to identify simulation-based tests that are unlikely to detect faults in the SDC software under test and skip them before their execution. Consequently, by filtering out those tests, SDC-Scissor reduces the number of long-running simulations to execute and drastically increases the cost-effectiveness of simulation-based testing of SDCs software. Our evaluation concerning two large datasets and around 12'000 tests showed that SDC-Scissor achieved a higher classification F1-score (between 47% and 90%) than a randomized baseline in identifying tests that lead to a fault and reduced the time spent running uninformative tests (speedup between 107% and 170%). Webpage & Video: https://github.com/ChristianBirchler/sdc-scissor