Michele Volpato

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The Temporal Constraint Satisfaction Problem with Uncertainty (TCSPU) and its disjunctive generalization, the Disjunctive Temporal Problem with Uncertainty (DTPU), are quantitative models for temporal reasoning that account simultaneously for disjunctive constraints and for events not under the control of the executing agent. Such a problem is Weakly(More)
Probabilistic model checking extends traditional model checking by incorporating quantitative information about the probability of system transitions. However, probabilistic models that describe interesting behavior are often too complex for straightforward analysis. Abstraction is one way to deal with this complexity: instead of analyzing the (“concrete”)(More)
In Angluin’s L∗ algorithm a learner constructs a sequence of hypotheses in order to learn a regular language. Each hypothesis is consistent with a larger set of observations and is described by a bigger model. From a behavioral perspective, however, a hypothesis is not always better than the previous one, in the sense that the minimal length of a(More)
This document contains the proofs to the theorems of the original paper. Some lemmas are also introduced. Theorem 1. Let qδ ∈ LT S(LI , LU ∪ {δ}) be a valid suspension automaton. Then, there exists a labelled transition system q ∈ LT S(LI , LU ) such that Straces(q) = traces(qδ). Proof. Follows directly from Theorem 2 in [18]. u t Algorithm 1 Construct(More)
Since testing is an expensive process, automatic testing with smart test selection has been proposed as a way to reduce such expense. Such a selection of tests can be done using specification coverage functions. Model-based ioco theory, however, uses test suites which are not suitable for easy computation of coverage because of interdependence of their test(More)
Constructing a model of a system for model-based testing, simulation, or model checking can be cumbersome for existing, third party, or legacy components. Active automata learning, a form of black-box reverse engineering, and in particular Angluin’s L algorithm, support the automatic inference of a model from a System Under Learning (SUL), through(More)
Since testing is an expensive process, test selection has been proposed as a way to reduce such expense. A good selection of tests can be done using specification coverage functions. Model-based ioco theory, however, uses test suites which are not suitable for computing coverage because of interdependence of their test cases. We define a new test suite that(More)
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