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Copyright @ 2011 John Wiley & SonsDistributed systems are usually composed of several distributed components that communicate with their environment through specific ports. When testing such a system we separately observe sequences of inputs and outputs at each port rather than a global sequence and potentially cannot reconstruct the global sequence that occurred. Typically, the users of such a system cannot synchronise their actions during use or testing. However, the use of the system might correspond to a sequence of\ud scenarios, where each scenario involves a sequence of interactions with the system that, for example, achieves a particular objective. When this is the case there is the potential for there to be a significant\ud delay between two scenarios and this effectively allows the users of the system to synchronise between scenarios. If we represent the specification of the global system by using a state-based notation, we\ud say that a scenario is any sequence of events that happens between two of these operations. We can encode scenarios in two different ways. The first approach consists of marking some of the states of the specification to denote these synchronisation points. It transpires that there are two ways to interpret such models and these lead to two implementation relations. The second approach consists\ud of adding a set of traces to the specification to represent the traces that correspond to scenarios. We show that these two approaches have similar expressive power by providing an encoding from marked states to sets of traces. In order to assess the appropriateness of our new framework, we show that it represents a conservative extension of previous implementation relations defined in the context of the distributed test architecture: if we onsider that all the states are marked then we simply obtain ioco (the classical relation for single-port systems) while if no state is marked then we obtain dioco (our previous relation for multi-port systems). Finally, we concentrate on the study of controllable\ud test cases, that is, test cases such that each local tester knows exactly when to apply inputs. We give two notions of controllable test cases, define an implementation relation for each of these notions, and relate them. We also show how we can decide whether a test case satisfies these conditions.Research partially supported by the Spanish MEC project TESIS (TIN2009-14312-C02-01), the UK EPSRC project Testing of Probabilistic and Stochastic Systems (EP/G032572/1), and the UCM-BSCH programme to fund research groups (GR58/08 - group number 910606)
Copyright @ 2011 John Wiley & SonsDistributed systems are usually composed of several distributed components that communicate with their environment through specific ports. When testing such a system we separately observe sequences of inputs and outputs at each port rather than a global sequence and potentially cannot reconstruct the global sequence that occurred. Typically, the users of such a system cannot synchronise their actions during use or testing. However, the use of the system might correspond to a sequence of\ud scenarios, where each scenario involves a sequence of interactions with the system that, for example, achieves a particular objective. When this is the case there is the potential for there to be a significant\ud delay between two scenarios and this effectively allows the users of the system to synchronise between scenarios. If we represent the specification of the global system by using a state-based notation, we\ud say that a scenario is any sequence of events that happens between two of these operations. We can encode scenarios in two different ways. The first approach consists of marking some of the states of the specification to denote these synchronisation points. It transpires that there are two ways to interpret such models and these lead to two implementation relations. The second approach consists\ud of adding a set of traces to the specification to represent the traces that correspond to scenarios. We show that these two approaches have similar expressive power by providing an encoding from marked states to sets of traces. In order to assess the appropriateness of our new framework, we show that it represents a conservative extension of previous implementation relations defined in the context of the distributed test architecture: if we onsider that all the states are marked then we simply obtain ioco (the classical relation for single-port systems) while if no state is marked then we obtain dioco (our previous relation for multi-port systems). Finally, we concentrate on the study of controllable\ud test cases, that is, test cases such that each local tester knows exactly when to apply inputs. We give two notions of controllable test cases, define an implementation relation for each of these notions, and relate them. We also show how we can decide whether a test case satisfies these conditions.Research partially supported by the Spanish MEC project TESIS (TIN2009-14312-C02-01), the UK EPSRC project Testing of Probabilistic and Stochastic Systems (EP/G032572/1), and the UCM-BSCH programme to fund research groups (GR58/08 - group number 910606)
There has been much interest in the testing of systems that have physically distributed interfaces and this has been encouraged by recent trends towards the use of such systems. Most formal work in this area has considered the testing of deterministic systems based on deterministic models. However, distributed systems are usually nondeterministic and often can be seen as probabilistic systems in which required or expected probabilities can be attached to the allowable events. This paper provides a formal testing framework for systems with physically distributed interfaces where nondeterministic decisions among alternatives are probabilistically quantified. It first considers testing from systems where there is a unique type of action. In this setting, a generative interpretation of probabilities is adequate and a formal framework to test these systems is provided. However, the observable events of a system are usually divided into inputs and outputs. In such situations it is necessary to use the reactive interpretation of probabilities.
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