Falsification has emerged as an important tool for simulation-based verification of autonomous systems. In this paper, we present extensions to the Scenic scenario specification language and VerifAI toolkit that improve the scalability of sampling-based falsification methods by using parallelism and extend falsification to multi-objective specifications. We first present a parallelized framework that is interfaced with both the simulation and sampling capabilities of Scenic and the falsification capabilities of VerifAI, reducing the execution time bottleneck inherently present in simulation-based testing. We then present an extension of VerifAI's falsification algorithms to support multi-objective optimization during sampling, using the concept of rulebooks to specify a preference ordering over multiple metrics that can be used to guide the counterexample search process. Lastly, we evaluate the benefits of these extensions with a comprehensive set of benchmarks written in the Scenic language.
@article{arxiv.2107.04164,
title = {Parallel and Multi-Objective Falsification with Scenic and VerifAI},
author = {Kesav Viswanadha and Edward Kim and Francis Indaheng and Daniel J. Fremont and Sanjit A. Seshia},
journal= {arXiv preprint arXiv:2107.04164},
year = {2021}
}