High-level Hybrid Systems Analysis with Hypy

Abstract

Hybrid systems play an important role in many application domains. A range of powerful analysis methods for this class of systems perform high-level analysis, where, iteratively, (1) a reachability computation is performed on a system model, (2) the result of the analysis is examined, and (3) the model is modified and the process repeats. For example, a well- known high-level analysis method is counter-example guided abstraction refinement (CEGAR), where, at each iteration, the model is refined based on the counter-example produced by the reachability computation. In this paper, we present hypy, a python library which strives to ease the development of high-level analysis approaches. Hypy provides the necessary machinery to run a number of up-to-date hybrid systems analysis tools, parse their outputs, and modify the models. The modifications are performed using HyST, a source-to-source model transformation framework, which supports output formats including SpaceEx, Flow*, dReach, and HyCreate. HyST, however, does not run reachability tools nor interpret their output. The developed hypy library fills this gap, providing an extendable and flexible architecture which simplifies development of complex analysis strategies. We demonstrate its practical potential on three non-CEGAR case studies: abstraction for parameter identification, generation of pseudo-invariants to reduce reachability overapproximation error, and completely automatic tool parameter tuning for the Flow* reachability tool.