The formal analysis of critical systems is supported by a vast space of modelling formalisms and tools. The variety of incompatible formats and tools however poses a significant challenge to practical adoption as well as continued research. In this paper, we propose the Jani model format and tool interaction protocol. The format is a metamodel based on networks of communicating automata and has been designed for ease of implementation without sacrificing readability. The purpose of the protocol is to provide a stable and uniform interface between tools such as model checkers, transformers, and user interfaces. Jani uses the Json data format, inheriting its ease of use and inherent extensibility. Jani initially targets, but is not limited to, quantitative model checking. Several existing tools now support the verification of Jani models, and automatic converters from a diverse set of higher-level modelling languages have been implemented. The ultimate purpose of Jani is to simplify tool development, encourage research cooperation, and pave the way towards a future competition in quantitative model checking.

Importance splitting is a technique to accelerate discrete event simulation when the value to estimate depends on the occurrence of rare events. It requires a guiding importance function typically defined in an ad hoc fashion by an expert in the field, who could choose an inadequate function. In this article we present a compositional and automatic technique to derive the importance function from the model description, and analyze different composition heuristics. This technique is linear in the number of modules, in contrast to the exponential nature of our previous proposal. This approach was compared to crude simulation and to importance splitting using typical ad hoc importance functions. A prototypical tool was developed and tested on several models, showing the feasibility and efficiency of the technique. POWVER TECHNICAL REPORT 2016-08 -THIS REPORT IS AN AUTHOR-GENERATED VERSION OF A PUBLICATION INVALUETOOLS 2016. PLEASE CITE THAT PUBLICATION INSTEAD OF THIS REPORT.Contrarily to the technique of [1], this way of calculating the importance function grows linearly with the number of modules that conform the system model.The paper is organized as follows. Sec. 2 explains the foundations of our specification language. Sec. 3 briefly describes the importance splitting technique and the RES-TART method. In Sec. 4 we introduce our technique for the compositional derivation of importance functions. Sec. 5 reports the performance of our technique on four different case studies. The paper concludes in Sec. 6.

Probabilistic model checking is a powerful tool for analysing probabilistic systems but it can only be efficiently applied to Markov models. Monte Carlo simulation provides an alternative for the generality of stochastic processes, but becomes infeasible if the value to estimate depends on the occurrence of rare events. To combat this problem, intelligent simulation strategies exist to lower the estimation variance and hence reduce the simulation time. Importance splitting is one such technique, but requires a guiding function typically defined in an ad hoc fashion by an expert in the field. We present an automatic derivation of the importance function from the model description. A prototypical tool was developed and tested on several Markov models, compared to analytically and numerically calculated results and to results of typical ad hoc importance functions, showing the feasibility and efficiency of this approach. The technique is easily adapted to general models like GSMPs.

We present a master-equation approach to the narrow-escape-time (NET) problem, i.e., the time needed for a particle contained in a confining domain with a single small or narrow opening to exit the domain. In this paper we introduce an alternative type of confining domain (to the usually spherical one) and we consider the diffusion process on a lattice rather than in continuous space. We have obtained analytic results for the basic quantity studied in the NET problem, the mean first-passage time, and we have studied its dependence in terms of the transition (desorption) probability over (from) the surface boundary and the confining domain dimensions. In addition to our analytical approach, we have implemented Monte Carlo simulations, finding excellent agreement between the theoretical results and simulations.