Parallel and Multi-Objective Falsification with Scenic and VerifAI
Kesav Viswanadha, Edward Kim, Francis Indaheng, Daniel J. Fremont and Sanjit A. Seshia
Abstract: 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.