This paper describes the NUXMV symbolic model checker for finiteand infinite-state synchronous transition systems. NUXMV is the evolution of the NUSMV open source model checker. It builds on and extends NUSMV along two main directions. For finite-state systems it complements the basic verification techniques of NUSMV with state-of-the-art verification algorithms. For infinitestate systems, it extends the NUSMV language with new data types, namely Integers and Reals, and it provides advanced SMT-based model checking techniques.Besides extended functionalities, NUXMV has been optimized in terms of performance to be competitive with the state of the art. NUXMV has been used in several industrial projects as verification back-end, and it is the basis for several extensions to cope with requirements analysis, contract based design, model checking of hybrid systems, safety assessment, and software model checking.This work was carried out within the D-MILS project, which is partially funded under the European Commission's Seventh Framework Programme (FP7).
Abstract. This paper describes the XSAP safety analysis platform. XSAP provides several model-based safety analysis features for finite-and infinite-state synchronous transition systems. In particular, it supports library-based definition of fault modes, an automatic model extension facility, generation of safety analysis artifacts such as Dynamic Fault Trees (DFTs) and Failure Mode and Effects Analysis (FMEA) tables. Moreover, it supports probabilistic evaluation of Fault Trees, failure propagation analysis using Timed Failure Propagation Graphs (TFPGs), and Common Cause Analysis (CCA). XSAP has been used in several industrial projects as verification back-end, and is currently being evaluated in a joint R&D Project involving FBK and The Boeing Company.
Temporal networks are data structures for representing and reasoning about temporal constraints on activities. Many kinds of temporal networks have been defined in the literature, differing in their expressiveness. The simplest kinds of networks have polynomial algorithms for determining their consistency or controllability, but corresponding algorithms for more expressive networks (e.g., those that include observation nodes or disjunctive constraints) have so far been unavailable. However, recent work has introduced a new approach to such algorithms based on translating temporal networks into Timed Game Automata (TGAs) and then using off-the-shelf software to synthesize execution strategies-or determine that none exist. So far, that approach has only been used on Simple Temporal Networks with Uncertainty, for which polynomial algorithms already exist. This paper extends the temporal-network-to-TGA approach to accommodate observation nodes and disjunctive constraints. Insodoing the paper presents, for the first time, sound and complete algorithms for checking the dynamic controllability of these more expressive networks. The translations also highlight the theoretical relationships between various kinds of temporal networks and the TGA model. The new algorithms have immediate applications in the workflow models being developed to automate business processes, including in the health-care domain.
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