Abstract
Statistical model checking avoids the state space explosion problem in verification and naturally supports complex non-Markovian formalisms. Yet as a simulation-based approach, its runtime becomes excessive in the presence of rare events, and it cannot soundly analyse nondeterministic models. In this article, we present modes: a statistical model checker that combines fully automated importance splitting to estimate the probabilities of rare events with smart lightweight scheduler sampling to approximate optimal schedulers in nondeterministic models. As part of the Modest Toolset, it supports a variety of input formalisms natively and via the Jani exchange format. A modular software architecture allows its various features to be flexibly combined. We highlight its capabilities using experiments across multi-core and distributed setups on three case studies and report on an extensive performance comparison with three current statistical model checkers.
Highlights
Statistical model checking (SMC [1,49,81]) is a formal verification technique for stochastic systems
We present modes, a statistical model checker that addresses both of the above challenges: It implements importance splitting [59] to efficiently estimate the probabilities of rare events, and lightweight scheduler sampling [60] to statistically approximate optimal schedulers
We describe the various methods implemented to make modes a correct and scalable statistical model checker that supports classes of models ranging from discrete-time Markov chains (DTMC [4]) to stochastic hybrid automata (SHA [32]) in Sect
Summary
Statistical model checking (SMC [1,49,81]) is a formal verification technique for stochastic systems. We present modes, a statistical model checker that addresses both of the above challenges: It implements importance splitting [59] to efficiently estimate the probabilities of rare events, and lightweight scheduler sampling [60] to statistically approximate optimal schedulers. Both methods can be combined to perform rare event simulation for nondeterministic models. The partial order and confluence-based methods have been replaced by LSS, enabling the simulation of non-spurious nondeterminism; automated importance splitting has been implemented for rare event simulation; support for MA and SHA has been added; the statistical evaluation methods have been extended and improved.
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