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In this paper, we present a metamodel for textual use case descriptions, structurally conforming to the UML, to specify the behavior of use cases in a flow-oriented manner. While being primarily targeted at supporting requirements engineers in creating consistent use case models, the metamodel defines a textual representation of use case behavior that is easily understandable for readers, who are unaware of the underlying metamodel. Hence, the known benefits of natural language use case descriptions are preserved. Being formalized, consistency between UML-based use case representations and their textual descriptions can be automatically ensured. With NaUTiluS we present an extensible, Eclipse-based toolkit, which offers integrated UML use case modeling support, as well as editing capabilities for their textual descriptions.

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Enterprise Architecture (EA) is a widely accepted means to ease the alignment of IS projects with enterprisewide objectives. One central artifact of EA are EA models, which provide a holistic view on the organization and support EA's stakeholder to create added value. As EA collects its data from different sources, the data can be contradictory. This work contributes to existing research by proposing a novel approach to deal with contradictory data without solving the thereby caused conflicts. In order to achieve this objective, we refine the Predictive, Probabilistic Architecture Modeling Framework (P 2 AMF) introduced by Johnson et al., which already incorporates a way to represent uncertainty regarding the existence of modelled entities. To make our technique usable, we generalize P 2 AMF from its UML/OCL notation to a graph presentation in order to apply it to EA models notated in arbitrary notations like ArchiMate. Furthermore, we add alternative scenarios in different versions along a time series to meet the requirements of a distributed EA evolution. To show the applicability of our approach, we developed a proof of concept prototype by implementing the proposed calculations and guidelines on a Neo4j graph database. Last, we argue that our approach meets the stated requirements of a distributed EA evolution.

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