Lightweight Statistical Model Checking in Nondeterministic Continuous Time

D’Argenio, Pedro R.; Hartmanns, Arnd; Sedwards, Sean

doi:10.1007/978-3-030-03421-4_22

Cited by 20 publications

(24 citation statements)

References 33 publications

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“…In [HMZ+12], candidates for optimal strategies are generated and gradually improved, but "at any given point we cannot quantify how close to optimal the candidate scheduler is" (cited from [HMZ+12]) and the algorithm "does not in general converge to the true optimum" (cited from [LST14]). Further, [LST14,DLST15,DHS18] randomly sample compact representation of strategies, resulting in useful lower bounds if ε-schedulers are frequent. [HPS+19] gives a convergent model-free algorithm (with no bounds on the current error) and identifies that the previous [SKC+14] "has two faults, the second of which also affects approaches [...] [HAK18,HAK19]".…”

Section: Related Workmentioning

confidence: 99%

PAC Statistical Model Checking for Markov Decision Processes and Stochastic Games

Ashok

Křetínský

Weininger

2019

Lecture Notes in Computer Science

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Statistical model checking (SMC) is a technique for analysis of probabilistic systems that may be (partially) unknown. We present an SMC algorithm for (unbounded) reachability yielding probably approximately correct (PAC) guarantees on the results. We consider both the setting (i) with no knowledge of the transition function (with the only quantity required a bound on the minimum transition probability) and (ii) with knowledge of the topology of the underlying graph. On the one hand, it is the first algorithm for stochastic games. On the other hand, it is the first practical algorithm even for Markov decision processes. Compared to previous approaches where PAC guarantees require running times longer than the age of universe even for systems with a handful of states, our algorithm often yields reasonably precise results within minutes, not requiring the knowledge of mixing time.

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Section: Related Workmentioning

confidence: 99%

PAC Statistical Model Checking for Markov Decision Processes and Stochastic Games

Ashok

Křetínský

Weininger

2019

Lecture Notes in Computer Science

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“…On the downside, we were unable to find counterexamples for some faulty variants and properties. This calls for future research, exploiting techniques to guide the simulation towards rare bugs/events [7,10,21] or towards uncovered variants relying, e.g., on distance-based sampling [22] or light-weight scheduling sampling [19]. Nevertheless, the positive outcome of our study is to show that SMC can act as a low-cost-high-reward alternative to exhaustive verification, which can provide thorough results in a majority of cases.…”

Section: Resultsmentioning

confidence: 88%

Statistical Model Checking for Variability-Intensive Systems

Cordy

Papadakis

Legay

2020

Fundamental Approaches to Software Engineering

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We propose a new Statistical Model Checking (SMC) method to discover bugs in variability-intensive systems (VIS). The state-space of such systems is exponential in the number of variants, which makes the verification problem harder than for classical systems. To reduce verification time, we sample executions from a featured transition systema model that represents jointly the state spaces of all variants. The combination of this compact representation and the inherent efficiency of SMC allows us to find bugs much faster (up to 16 times according to our experiments) than other methods. As any simulation-based approach, however, the risk of Type-1 error exists. We provide a lower bound and an upper bound for the number of simulations to perform to achieve the desired level of confidence. Our empirical study involving 59 properties over three case studies reveals that our method manages to discover all variants violating 41 of the properties. This indicates that SMC can act as a low-cost-high-reward method for verifying VIS.

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“…Finally, an avenue that avoids storing a (complete) model are simulation-based approaches (statistical model checking [2]) and variants of reinforcement learning, possibly with neural networks. For MDPs, these approaches yield weak statistical guarantees [20], but may provide good oracles.…”

Section: Statistical Methods and (Deep) Reinforcement Learningmentioning

confidence: 99%