International audience

ABSTRACT To assess test quality, mutation testing (MT) creates mutants by injecting artificial faults into the system and evaluates the ability of tests to distinguish these mutants. Tests distinguishing more mutants have also been proven empirically to detect more real faults. MT has been applied to many domains. We focus on MT for timed safety‐critical systems modelled as Timed Automata (TA). While powerful, MT usually yields equivalent and duplicate mutants, the former having the same behaviour as the original system and the latter other mutants. Such useless mutants bring no value, waste execution time and can be difficult to detect. We integrate useless mutant detection and removal strategies in our mutation framework MUPPAAL. MUPPAAL leverages existing equivalence‐avoiding mutation operators and focuses on detecting mutant duplicates using a scalable bisimulation algorithm and a fast approximate one based on biased simulation. We also demonstrate how to design an operator that reduces the occurrence of mutant duplicates. We evaluate MUPPAAL on six systems, demonstrating that (1) mutant duplicates account for up to 32% of all generated mutants, (2) our bisimulation approach scales effectively with these systems and (3) biased simulations further enhance performance. Our heuristic is 10 times faster than bisimulation and limits the exploration to two times the number of exact duplicates compared to up to 10 times for the baseline.

We introduce a DeGroot-based model for opinion dynamics in social networks. A community of agents is represented as a weighted directed graph whose edges indicate how much agents influence one another. The model is formalized using labeled transition systems, henceforth called opinion transition systems (OTS), whose states represent the agents' opinions and whose actions are the edges of the influence graph. If a transition labeled (i,j) is performed, agent j updates their opinion taking into account the opinion of agent i and the influence i has over j. We study (convergence to) opinion consensus among the agents of strongly-connected graphs with influence values in the interval (0,1). We show that consensus cannot be guaranteed under the standard strong fairness assumption on transition systems. We derive that consensus is guaranteed under a stronger notion from the literature of concurrent systems; bounded fairness. We argue that bounded-fairness is too strong of a notion for consensus as it almost surely rules out random runs and it is not a constructive liveness property. We introduce a weaker fairness notion, called m-bounded fairness, and show that it guarantees consensus. The new notion includes almost surely all random runs and it is a constructive liveness property. Finally, we consider OTS with dynamic influence and show convergence to consensus holds under m-bounded fairness if the influence changes within a fixed interval [L,U] with 0 < L < U < 1. We illustrate OTS with examples and simulations, offering insights into opinion formation under fairness and dynamic influence.

: In this paper we present a new access control method called Granular. Granular extends traditional access control methods in order to manage XML-like documents by means of a rules set that allows easy administration of the system describing the accessed content using a suitable set of rights. One of the main novelties is the definition of closed and open authorizations, which provides a more precise way of controlling document access. It allows for an early control of possible conflicts between different kinds of accesses. Formal definitions of Granular actions and rules are presented in this paper, and the implementation of Granular is discussed.