On uniformly sampling traces of a transition system

Chakraborty, Supratik; Shrotri, Aditya A.; Vardi, Moshe Y.

doi:10.1145/3400302.3415707

Cited by 2 publications

(3 citation statements)

References 33 publications

(32 reference statements)

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“…Unfortunately, it is well known (see, e.g., [17] and citations thereof) that, in case of non-trivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputs in general fail in extracting scenarios according to a given distribution (e.g., uniformly). Also, such approaches can be very inefficient to produce at all scenarios that are both legal (with respect to SUV assumptions and requirements on its environment) and of interest (with respect to the additional constraints).…”

Section: Motivationmentioning

confidence: 99%

“…Several approaches, both explicit (see, e.g., [25], [70]) and symbolic (see, e.g., [2], [17]) have been proposed to sample uniformly at random finite paths from a finite-state combinatorial structure (automaton, combinatorial circuit, or SAT instance). Differently from such methods, since we focus on finite prefixes of infinite paths, we first need to restrict the input monitor (a FSM or a collection thereof) to its non-blocking portion, via the concept of Scenario Generator, by essentially computing the most liberal supervisory controller of it.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Motivationmentioning

confidence: 99%

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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“…Along this line, the closest work to ours is [4] where the temporal patterns of input signal spaces are constrained by timed automata which entails particular polytopic constraints that are nevertheless simpler than the constraints yielded by shape expressions. Uniform sampling of traces satisfying given constraints has been considered in constrained random verification, for different specification and modeling formalisms, such as uniform and nearly-uniform sampling satisfying assignments of Boolean formulas [8,7,20], uniformly sampling traces of networks of automata [6], timed automata [3], transition systems [9]. Monte-Carlo LTL model-checking introduced in [19] was enhanced with a uniform version [33].…”

Section: Related Workmentioning

confidence: 99%

Sampling of shape expressions with ShapEx

Basset

Dang

Gigler

et al. 2021

Proceedings of the 19th ACM-IEEE International Conference on Formal Methods and Models for System Design

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Cyber-physical systems (CPS) are increasingly becoming driven by data, using multiple types of sensors to capture huge amounts of data. Extraction and characterization of useful information from big streams of data is a challenging problem. Shape expressions facilitate formal specification of rich temporal patterns encountered in time series as well as in behaviors of CPS. In this paper, we introduce a method for systematically sampling shape expressions. The proposed approach combines methods for uniform sampling of automata (for exploring qualitative shapes) with hit-and-run Monte Carlo sampling procedures (for exploring multi-dimensional parameter spaces defined by sets of possibly non-linear constraints). We study and implement several possible solutions and evaluate them in the context of visualisation and testing applications.

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On uniformly sampling traces of a transition system

Cited by 2 publications

References 33 publications

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

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

Sampling of shape expressions with ShapEx

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