Contractual Testing

Variants of must testing approach have been successfully applied in Service Oriented Computing for capturing compliance between (contracts exposed by) a client and a service and for characterising safe replacement, namely the fact that compliance is preserved when a service exposing a ’smaller’ contract is replaced by another one with a ’larger’ contract. Nevertheless, in multi-party interactions, partners often lack full coordination capabilities. Such a scenario calls for less discriminating notions of testing in which observers are, e.g., the description of uncoordinated multiparty contexts or contexts that are unable to observe the complete behaviour of the process under test. In this paper we propose an extended notion of must preorder, called contractual preorder, according to which contracts are compared according to their ability to pass only the tests belonging to a given set. We show the generality of our framework by proving that preorders induced by existing notions of compliance in a distributed setting are instances of the contractual preorder when restricting to suitable sets of observers

Distributed Verification of Rare Properties using Importance Splitting Observers

Rare properties remain a challenge for statistical model checking (SMC) due to the quadratic scaling of variance with rarity. We address this with a variance reduction framework based on lightweight importance splitting observers. These expose the model-property automaton to allow the construction of score functions for high performance algorithms. The confidence intervals defined for importance splitting make it appealing for SMC, but optimising its performance in the standard way makes distribution inefficient. We show how it is possible to achieve equivalently good results in less time by distributing simpler algorithms. We first explore the challenges posed by importance splitting and present an algorithm optimised for distribution. We then define a specific bounded time logic that is compiled into memory-efficient observers to monitor executions. Finally, we demonstrate our framework on a number of challenging case studies

Reconstructing initial data using iterative observers for wave type systems. A numerical analysis

A new iterative algorithm for solving initial data inverse problems from partial observations has been recently Proposed in Ramdani et al. (Automatica 46(10), 1616–1625, 2010). Based on the concept of observers (also called Luenberger observers), this algorithm covers a large class of abstract evolution PDE’s. In this paper, we are concerned with the convergence analysis of this algorithm. More precisely, we provide a complete numerical analysis for semi-discrete (in space) and fully discrete approximations derived using finite elements in space and an implicit Euler method in time. The analysis is carried out for abstract Schrödinger and wave conservative systems with bounded observation (locally distributed)