Methods for service specification should be simple and intuitive. At the same time they should be precise and allow early validations to be performed, in order to detect inconsistencies as early as possible in the service development cycle. In this paper we present a service specification approach based on UML 2.0 collaborations. It aims to be a constructive approach, rather than a corrective one, as it is intended to promote understanding and help reducing the number of specification errors. We also address the detection of implied scenarios from collaboration-based service specifications, and propose an approach that limits the state explosion problem. This is possible since the detection analysis is modular and it is performed at a high-level of abstraction.
Methods for service specification should be simple and intuitive. At the same time they should be precise and allow early validation and detection of inconsistencies. UML 2.0 collaborations enable a systematic and structured way to provide overview of distributed services, and decompose cross-cutting service behaviour into features and interfaces by means of collaboration-uses. To fully take advantage of the possibilities thus opened, a way to compose (i.e. choreograph) the joint collaboration behaviour is needed. So-called collaboration goal sequences have been introduced for this purpose. They describe the behavioural composition of collaboration-uses (modeling interface behaviour and features) within a composite collaboration. In this paper we propose a formal semantics for collaboration goal sequences by means of hierarchical coloured Petri-nets (HCPNs). We then show how tools available for HCPNs can be used to automatically analyse goal sequences in order to detect implied scenarios.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.