On improving rare event simulation for probabilistic safety analysis

Gonschorek, Tim; Rabeler, Ben; Ortmeier, Frank; Schomburg, Dirk

doi:10.1145/3127041.3127057

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…When exhaustive verification is not a viable option, given the huge number of scenarios of interest, simulationbased statistical model checking is often preferred, in order to compute statistically-sound information about the SUV properties of interest from a random sample of the possible scenarios, see, e.g., [7], [8], [10], [18], [19], [20], [23], [23], [24],…”

Section: Related Workmentioning

confidence: 99%

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Optimizing Highly-Parallel Simulation-Based Verification of Cyber-Physical Systems

Mancini

Melatti

Tronci

2023

IIEEE Trans. Software Eng.

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Cyber-Physical Systems (CPSs), i.e., systems comprising both software and physical components, arise in many industry-relevant application domains and often mission-or safety-critical. System-Level Verification (SLV) of CPSs aims at certifying that given (e.g., safety or liveness) specifications are met, or at estimating the value of some Key Performance Indicators, when the system runs in its operational environment, that is in presence of inputs (from the user or other systems) and/or of additional, uncontrolled disturbances. In order to enable SLV of complex systems from the early design phases, the currently most adopted approach envisions the simulation of a system model under the (time bounded) operational scenarios deemed of interest. Unfortunately, simulation-based SLV can be computationally prohibitive (years of sequential simulation), since system model simulation is computationally intensive and the set of scenarios of interest can be extremely large.In this article, we present a technique that, given a collection of scenarios of interest (extracted from mass-storage databases or from symbolic structures like constraint-based scenario generators), computes parallel shortest simulation campaigns, which drive a possibly large number of system model simulators running in parallel in a HPC infrastructure through all (and only) those scenarios in the user-defined (possibly random) order, by wisely avoiding multiple simulations of repeated trajectories, and thus minimising the overall completion time, compatibly with the available simulator memory capacity. Our experiments on SLV of Modelica/FMU and Simulink case study models with up to almost 200 million scenarios show that our optimisation yields speedups as high as 8×. This, together with the enabled massive parallelisation, makes practically viable (a few weeks in a HPC infrastructure) verification tasks (both statistical and exhaustive, with respect to the given set of scenarios) which would otherwise take inconceivably long time.

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Section: Related Workmentioning

confidence: 99%

“…4. In both cases, the values of ntraces (prefix) for each LSPT node prefix ⊑ lsp (including both lsp and the empty prefix, if the latter is in the LSPT) are incremented by 1, in order to take into account their occurrence in trace u (lines [17][18][19][20].…”

Section: C1 Randomised Simulation Campaignsmentioning

confidence: 99%

Optimizing Highly-Parallel Simulation-Based Verification of Cyber-Physical Systems

Mancini

Melatti

Tronci

2023

IIEEE Trans. Software Eng.

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“…Simulation-based approaches to SMC have been also widely investigated, e.g., [11], [14], [16], [26], [28], [29], [35], [36], [72], [73]. Simulink models for CPS have been studied in [21], [74], mixed-analog circuits have been analysed in [18].…”

Section: Related Workmentioning

confidence: 99%

Any-Horizon Uniform Random Sampling and Enumeration of Constrained Scenarios for Simulation-Based Formal Verification

Mancini

Melatti

Tronci

2022

IIEEE Trans. Software Eng.

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Model-based approaches to the verification of non-terminating Cyber-Physical Systems (CPSs) usually rely on numerical simulation of the System Under Verification (SUV) model under input scenarios of possibly varying duration, chosen among those satisfying given constraints. Such constraints typically stem from requirements (or assumptions) on the SUV inputs and its operational environment as well as from the enforcement of additional conditions aiming at, e.g., prioritising the (often extremely long) verification activity, by, e.g., focusing on scenarios explicitly exercising selected requirements, or avoiding vacuity in their satisfaction. In this setting, the possibility to efficiently sample at random (with a known distribution, e.g., uniformly) within, or to efficiently enumerate (possibly in a uniformly random order) scenarios among those satisfying all the given constraints is a key enabler for the practical viability of the verification process, e.g., via simulation-based statistical model checking. Unfortunately, in case of non-trivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputsin general fail in extracting scenarios according to a given distribution (e.g., uniformly), and can be very inefficient to produce at all scenarios that are both legal (with respect to SUV assumptions) and of interest (with respect to the additional constraints). For example, in our case studies, up to 91% of the scenarios generated using such iterative approaches would need to be neglected. In this article, we show how, given a set of constraints on the input scenarios succinctly defined by multiple finite memory monitors, a data structure (scenario generator ) can be synthesised, from which any-horizon scenarios satisfying the input constraints can be efficiently extracted by (possibly uniform) random sampling or (randomised) enumeration. Our approach enables seamless support to virtually all simulation-based approaches to CPS verification, ranging from simple random testing to statistical model checking and formal (i.e., exhaustive) verification, when a suitable bound on the horizon or an iterative horizon enlargement strategy is defined, as in the spirit of bounded model checking.

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On improving rare event simulation for probabilistic safety analysis

Cited by 2 publications

References 29 publications

Optimizing Highly-Parallel Simulation-Based Verification of Cyber-Physical Systems

Optimizing Highly-Parallel Simulation-Based Verification of Cyber-Physical Systems

Any-Horizon Uniform Random Sampling and Enumeration of Constrained Scenarios for Simulation-Based Formal Verification

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