Presentation of the 9th Edition of the Model Checking Contest.

International audience; The Model Checking Contest (MCC) is an annual competition of software tools for model checking. Tools must process an increasing benchmark gathered from the whole community and may participate in various examinations: state space generation, computation of global properties, computation of some upper bounds in the model, evaluation of reachability formulas, evaluation of CTL formulas, and evaluation of LTL formulas.For each examination and each model instance, participating tools are provided with up to 3600 s and 16 gigabyte of memory. Then, tool answers are analyzed and confronted to the results produced by other competing tools to detect diverging answers (which are quite rare at this stage of the competition, and lead to penalties).For each examination, golden, silver, and bronze medals are attributed to the three best tools. CPU usage and memory consumption are reported, which is also valuable information for tool developers

Guarded Autonomous Transitions Increase Conciseness and Expressiveness of Timed Automata

International audienceTimed Automata (TA) are an appropriate model for specifying timed requirements for Continuous Time Markov Chains (CTMC). However in order to keep tractable the model checking of a TA over a CTMC, temporal logics based on TA, like CSL TA , restrict TA to have a single clock and to be deterministic (DTA). Different variants of DTAs have been proposed to address the issue of their expressiveness and conciseness. Here we study the effect of two possible features: (1) autonomous transitions which are triggered by time elapsing in addition to synchronized transitions and (2) transitions guarded by propositional formulas instead of propositional formulas guarding locations. We first show that autonomous guarded transitions increase the expressiveness of DTAs (as already shown for guarded locations). Then we identify a hierarchy of DTAs subclasses all equivalent to DTAs without guarded autonomous transitions and we analyze their respective conciseness. In particular we show that eliminating resets in autonomous transitions implies an exponential blow-up, while eliminating autonomous transitions without reset can be performed in polynomial time if decision diagrams are used. Finally we compare TA with guarded transitions to TA with guarded locations showing that the former model is exponentially more concise than the latter one

Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms

Continuous-time Markov chains with alarms (ACTMCs) allow for alarm events that can be non-exponentially distributed. Within parametric ACTMCs, the parameters of alarm-event distributions are not given explicitly and can be subject of parameter synthesis. An algorithm solving the $\varepsilon$-optimal parameter synthesis problem for parametric ACTMCs with long-run average optimization objectives is presented. Our approach is based on reduction of the problem to finding long-run average optimal strategies in semi-Markov decision processes (semi-MDPs) and sufficient discretization of parameter (i.e., action) space. Since the set of actions in the discretized semi-MDP can be very large, a straightforward approach based on explicit action-space construction fails to solve even simple instances of the problem. The presented algorithm uses an enhanced policy iteration on symbolic representations of the action space. The soundness of the algorithm is established for parametric ACTMCs with alarm-event distributions satisfying four mild assumptions that are shown to hold for uniform, Dirac and Weibull distributions in particular, but are satisfied for many other distributions as well. An experimental implementation shows that the symbolic technique substantially improves the efficiency of the synthesis algorithm and allows to solve instances of realistic size.Comment: This article is a full version of a paper accepted to the Conference on Quantitative Evaluation of SysTems (QEST) 201

The quantitative verification benchmark set

A great publication

One Net Fits All: A unifying semantics of Dynamic Fault Trees using GSPNs

Dynamic Fault Trees (DFTs) are a prominent model in reliability engineering. They are strictly more expressive than static fault trees, but this comes at a price: their interpretation is non-trivial and leaves quite some freedom. This paper presents a GSPN semantics for DFTs. This semantics is rather simple and compositional. The key feature is that this GSPN semantics unifies all existing DFT semantics from the literature. All semantic variants can be obtained by choosing appropriate priorities and treatment of non-determinism.Comment: Accepted at Petri Nets 201