Henning Agt

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We propose a model-based methodology for integration of heterogeneous distributed systems, based on the multi-level modeling abstractions, automated conflict analysis and connector code generation. The focus in this paper is on the metamodeling foundation necessary for this process, and consequently we introduce computation independent, platform specific,(More)
We observe that small and medium enterprises who wish to adopt domain specific modeling techniques do so under different preconditions and with different expectations. In our report, we categorize our observations made in 7 different industrial branches. Further, we present the current state of our solution to provide guidance to both ends of stakeholders(More)
In this study we compared the feasibility, internal structure and psychometric characteristics (internal consistency, test-retest reliability, construct validity) of two widely used generic health status measures, i.e. the Nottingham Health Profile (NHP) and the Sickness Impact Profile (SIP) when employed among a sample of patients on renal dialysis (n=63).(More)
Software integration is one of the major needs as well as cost driving factors in the software industry today. Still, very few established methodologies exist, especially those addressing integration with respect to non-functional properties. Industry studies show that disregarded and hidden non-functional incompatibilities between systems and their(More)
In model-driven engineering, domain-specific languages (DSLs) play an important role in providing well-defined environments for modeling different aspects of a system. Detailed knowledge of the application domain as well as expertise in language engineering is required to create new languages. This research work proposes automated knowledge acquisition to(More)
In order to support the domain modeling process in model-based software development, we automatically create large networks of semantically related terms from natural language. Using part-of-speech tagging, lexical patterns and co-occurrence analysis, and several semantic improvement algorithms, we construct SemNet, a network of approximately 2.7 million(More)
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