A bounded statistical approach for model checking of unbounded until properties

He, Ru; Jennings, Paul C.; Basu, Samik; Ghosh, Arka P.; Wu, Huaiqing

doi:10.1145/1858996.1859043

Cited by 16 publications

(17 citation statements)

References 16 publications

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“…For example, it would be interesting to investigate how to handle schedulers with memory. Another potentially interesting research direction would be adapting the work in [28] and [29] to extend our algorithm to allow the verification of temporal properties without bounds.…”

Section: Discussionmentioning

confidence: 99%

Statistical Model Checking for Markov Decision Processes

Henriques

Martins

Zuliani

et al. 2012

2012 Ninth International Conference on Quantitative Evaluation of Systems

100

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Abstract-Statistical Model Checking (SMC) is a computationally very efficient verification technique based on selective system sampling. One well identified shortcoming of SMC is that, unlike probabilistic model checking, it cannot be applied to systems featuring nondeterminism, such as Markov Decision Processes (MDP). We address this limitation by developing an algorithm that resolves nondeterminism probabilistically, and then uses multiple rounds of sampling and Reinforcement Learning to provably improve resolutions of nondeterminism with respect to satisfying a Bounded Linear Temporal Logic (BLTL) property. Our algorithm thus reduces an MDP to a fully probabilistic Markov chain on which SMC may be applied to give an approximate solution to the problem of checking the probabilistic BLTL property. We integrate our algorithm in a parallelised modification of the PRISM simulation framework. Extensive validation with both new and PRISM benchmarks demonstrates that the approach scales very well in scenarios where symbolic algorithms fail to do so.

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Section: Discussionmentioning

confidence: 99%

Statistical Model Checking for Markov Decision Processes

Henriques

Martins

Zuliani

et al. 2012

2012 Ninth International Conference on Quantitative Evaluation of Systems

100

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“…However, one of them may require an inordinate number of samples to produce results; while the other relies on reachability analysis, which requires the full model to be constructed, relinquishing one of the key advantages of Monte Carlo model checking over probabilistic model checking. The work in [He et al 2010] also presents a bounded statistical approach for checking unbounded properties that does not need the full model to be constructed. However, the bound on the necessary trace length is excessively large, as traces may be as long as the total number of states in the model.…”

Section: Discussion and Related Workmentioning

confidence: 99%

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Pavese

Braberman

Uchitel

2016

ACM Trans. Softw. Eng. Methodol.

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College London and CONICETModel-based reliability estimation of systems can provide useful insights early in the development process. However, computational complexity of estimating metrics such as mean time to first failure (MTTF), turnaround time (TAT), or other domain-based quantitative measures can be prohibitive both in time, space and precision. In this paper we present an alternative to exhaustive model exploration-as in probabilistic model checking-and partial random exploration-as in statistical model checking. Our hypothesis is that a (carefully crafted) partial systematic exploration of a system model can provide better bounds for these quantitative model metrics at lower computation cost. We present a novel automated technique for metric estimation that combines simulation, invariant inference and probabilistic model checking. Simulation produces a probabilistically relevant set of traces from which a state invariant is inferred. The invariant characterises a partial model which is then exhaustively explored using probabilistic model checking. We report on experiments that suggest that metric estimation using this technique (for both fully probabilistic models and those exhibiting non-determinism) can be more effective than (full model) probabilistic and statistical model checking especially for system models where the events of interest are rare.

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“…SMC approaches to Markov chain verification, organised by (i) the class of verifiable properties, and (ii) by the required information about the Markov chain, where pmin is the minimum transition probability, |S| is the number of states, and λ is the second largest eigenvalue of the chain. LTL, mean payoff × here [3] (LTL) ♦, U × here -"- [25] [25], [8] bounded e.g. [27,20] no info pmin |S|, pmin λ topology (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…For completeness, we survey briefly other related SMC approaches. SMC of unbounded properties, usually "unbounded until" properties, was first considered in [9] and the first approach was proposed in [21], but observed incorrect in [8]. Notably, in [25] two approaches are described.…”

Section: Introductionmentioning

confidence: 99%

“…The second approach of [25] requires the knowledge of the chain's topology, which is used to transform the chain so that all potentially infinite paths are eliminated. In [8], a similar transformation is performed, again requiring knowledge of the topology. The (pre)processing of the state space required by the topology-aware methods, as well as by traditional numerical methods for Markov chain analysis, is a major practical hurdle for large (or unknown) state spaces.…”

Section: Introductionmentioning

confidence: 99%

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Faster Statistical Model Checking for Unbounded Temporal Properties

Daca

Henzinger

Křetínský

et al. 2016

Lecture Notes in Computer Science

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Abstract. We present a new algorithm for the statistical model checking of Markov chains with respect to unbounded temporal properties, including full linear temporal logic. The main idea is that we monitor each simulation run on the fly, in order to detect quickly if a bottom strongly connected component is entered with high probability, in which case the simulation run can be terminated early. As a result, our simulation runs are often much shorter than required by termination bounds that are computed a priori for a desired level of confidence on a large state space. In comparison to previous algorithms for statistical model checking our method is not only faster in many cases but also requires less information about the system, namely, only the minimum transition probability that occurs in the Markov chain. In addition, our method can be generalised to unbounded quantitative properties such as mean-payoff bounds. IntroductionTraditional numerical algorithms for the verification of Markov chains may be computationally intense or inapplicable, when facing a large state space or limited knowledge about the chain. To this end, statistical algorithms are used as a powerful alternative. Statistical model checking (SMC) typically refers to approaches where (i) finite paths of the Markov chain are sampled a finite number of times, (ii) the property of interest is verified for each sampled path (e.g. state r is reached), and (iii) hypothesis testing or statistical estimation is used to infer conclusions (e.g. state r is reached with probability at most 0.5) and give statistical guarantees (e.g. the conclusion is valid with 99% confidence). SMC approaches differ in (a) the class of properties they can verify (e.g. bounded or unbounded properties), (b) the strength of statistical guarantees they provide ⋆

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A bounded statistical approach for model checking of unbounded until properties

Cited by 16 publications

References 16 publications

Statistical Model Checking for Markov Decision Processes

Statistical Model Checking for Markov Decision Processes

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Faster Statistical Model Checking for Unbounded Temporal Properties

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