Abstract. The goal of this roadmap paper is to summarize the state-ofthe-art and to identify critical challenges for the systematic software engineering of self-adaptive systems. The paper is partitioned into four parts, one for each of the identified essential views of self-adaptation: modelling dimensions, requirements, engineering, and assurances. For each view, we present the state-of-the-art and the challenges that our community must address. This roadmap paper is a result of the Dagstuhl Seminar 08031 on "Software Engineering for Self-Adaptive Systems, " which took place in January 2008.

Abstract. The goal of this roadmap paper is to summarize the stateof-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

Semantic Web services envision the automated discovery and selection of Web services. This can be realised by adding semantic information to advertised services and service requirements. The discovery and selection process finds matches between requirements and advertisements according to their semantic description. Based on the Web Ontology Language (OWL) an ontology for Web services (OWL-S) was introduced to standardise their semantic description. There are already some approaches available for matching of service requirements with service advertisements according to such an ontology. We propose an algorithm, which ranks the matching degree of service descriptions according to OWL-S. Different matching degrees are achieved based on the contravariance of the input and output types for requested and advertised services. Furthermore, additional elements of the service description, such as the service category, are either covered by reasoning processes or, such as quality of service constraints, by custom matching rules. Contrary to mechanisms that return only success or fail, ranked results provide criteria for the selection of a service among a large set of results. With such a discovery mechanism additional Web services can be found that might have normally been ignored.

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Semantic web service composition is about finding services from a repository that are able to accomplish a specified task if executed. The task is defined in a form of a composition request which contains a set of available input parameters and a set of wanted output parameters. Instead of the parameter values, concepts from an ontology describing their semantics are passed to the composition engine. The parameters of the services in the repository the composer works on are semantically annotated in the same way as the parameters in the request. The composer then finds a sequence of services, called a composition. If the input parameters given in the request are provided, the services of this sequence can subsequently be executed and will finally produce the wanted output parameters.In this paper, three different approaches to semantic web service composition are formally defined and compared with each other: an uninformed search in form of an IDDFS algorithm, a greedy informed search based on heuristic functions, and a multiobjective genetic algorithm.