This work presents version 1.3 of the Finite Improbability Generator (FIG): a statistical model checker to estimate transient and steady-state reachability properties in stochastic automata. Specialised in rare event simulation via importance splitting, FIG implements RESTART and Fixed Effort algorithms. Its distinctive feature is push-button automation: users need not define an importance function, because FIG can derive it from the property query and model specifications. The input models are Input/Output Stochastic Automata with Urgency, written either in the native IOSA syntax or in the JANI exchange format. The theory backing FIG has demonstrated good efficiency, comparable to optimal importance splitting implemented ad hoc for specific systems. Written in C++, FIG is FOSS released under the GPLv3 in https://git.cs.famaf.unc.edu.ar/dsg/fig.

FIG: the Finite Improbability Generator v1.3 / Budde, C. E.. - In: ACM SIGMETRICS PERFORMANCE EVALUATION REVIEW. - ISSN 1557-9484. - 49:4(2022), pp. 59-64. [10.1145/3543146.3543160]

FIG: the Finite Improbability Generator v1.3

Budde C. E.
2022-01-01

Abstract

This work presents version 1.3 of the Finite Improbability Generator (FIG): a statistical model checker to estimate transient and steady-state reachability properties in stochastic automata. Specialised in rare event simulation via importance splitting, FIG implements RESTART and Fixed Effort algorithms. Its distinctive feature is push-button automation: users need not define an importance function, because FIG can derive it from the property query and model specifications. The input models are Input/Output Stochastic Automata with Urgency, written either in the native IOSA syntax or in the JANI exchange format. The theory backing FIG has demonstrated good efficiency, comparable to optimal importance splitting implemented ad hoc for specific systems. Written in C++, FIG is FOSS released under the GPLv3 in https://git.cs.famaf.unc.edu.ar/dsg/fig.
2022
4
Budde, C. E.
FIG: the Finite Improbability Generator v1.3 / Budde, C. E.. - In: ACM SIGMETRICS PERFORMANCE EVALUATION REVIEW. - ISSN 1557-9484. - 49:4(2022), pp. 59-64. [10.1145/3543146.3543160]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/357436
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