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Falsification has emerged as an important tool for simulation-basedverification of autonomous systems. In this paper, we present extensions to theScenic scenario specification language and VerifAI toolkit that improve thescalability of sampling-based falsification methods by using parallelism andextend falsification to multi-objective specifications. We first present aparallelized framework that is interfaced with both the simulation and samplingcapabilities of Scenic and the falsification capabilities of VerifAI, reducingthe execution time bottleneck inherently present in simulation-based testing.We then present an extension of VerifAI's falsification algorithms to supportmulti-objective optimization during sampling, using the concept of rulebooks tospecify a preference ordering over multiple metrics that can be used to guidethe counterexample search process. Lastly, we evaluate the benefits of theseextensions with a comprehensive set of benchmarks written in the Sceniclanguage.

Parallel and Multi-Objective Falsification with Scenic and VerifAI