The Probabilistic Model Checking Landscape

Abstract

Randomization is a key element in sequential and distributed computing. Reasoning about randomized algorithms is highly nontrivial. In the 1980s, this initiated first proof methods, logics, and model-checking algorithms. The field of probabilistic verification has developed considerably since then. This paper surveys the algorithmic verification of probabilistic models, in particular probabilistic model checking. We provide an informal account of the main models, the underlying algorithms, applications from reliability and dependability analysis-and beyond-and describe recent developments towards automated parameter synthesis.

Stochastic Model Checking for Predicting Component Failures and Service Availability

“…13 running on an Android tablet. The system is a clientserver architecture, implemented in Node.js 7 that relies on remote PRISM instances for heavyweight computations. The web app supports a simple interface for users unfamiliar with the underlying model, enabling simple instantiation, using sliders, of parameters such as rates for events, sector topologies, percentage of quick repairs, and current configuration.…”

“…Default values are provided. Analysis results 7. https://nodejs.org/ displayed on the device are in the formats used in this paper (e.g.…”

“…The field of probabilistic verification (in particular by model checking) has grown considerably since the 1980s (see [7]). But to our knowledge there has been little work using these methods to predict future service availability and inform operational decisions in the presence of component failures.…”

Are Parametric Markov Chains Monotonic?

“…If assumptions are needed (l. [21][22][23][24][25][26], then we push the three assumptions to the Queue. Therefore, l. 27, should not be executed, so we continue to the next iteration of the while loop after adding assumptions (l. 26) At the end, we check for global monotonicity (l. [29][30][31][32][33][34][35][36][37].…”

“…Probabilistic model checking [3,35] takes as input a Markov model together with a specification typically given in a probabilistic extension of LTL or CTL. The key problem is computing the reachability probability to reach a set of target states.…”