2,912 research outputs found

    Towards Effective SysML Model Reuse

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    The Systems Modeling Language (SysML) is spreading very fast. Most modelling tool vendors support it and practitioners have adopted it for Systems Engineering. The number of SysML models is growing, increasing the need for and the potential benefit from platforms that allow a user to reuse the knowledge represented in the models. However, SysML model reuse remains challenging. Each tool has its own implementation of SysML, hindering reuse between tools. The search capabilities of most tools are also very limited and finding reusable models can be difficult. This paper presents our vision and initial work towards enabling an effective reuse of the knowledge contained in SysML models. The proposed solution is based on a universal information representation model called RSHP and on existing technology for indexing and retrieval. The solution has been used to index models of all SysML diagram types and preliminary validated with requirements diagrams. The results from the validation show that the solution has very high precision and recall. This makes us confident that the solution can be a suitable means for effective SysML model reuse.European CommissionThe research leading to this paper has received funding from the AMASS project (H2020-ECSEL grant agreement no 692474; Spain's MINECO ref. PCIN-2015-262)

    Software similarity measurements using UML diagrams: A systematic literature review

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    Every piece of software uses a model to derive its operational, auxiliary, and functional procedures. Unified Modeling Language (UML) is a standard displaying language for determining, recording, and building a software product. Several algorithms have been used by researchers to measure similarities between UML artifacts. However, there no literature studies have considered measurements of UML diagram similarities. This paper presents the results of a systematic literature review concerning similarity measurements between the UML diagrams of different software products. The study reviews and identifies similarity measurements of UML artifacts, with class diagram, sequence diagram, statechart diagram, and use case diagram being UML diagrams that are widely used as research objects for measuring similarity. Measuring similarity enables resolution of the problem domains of software reuse, similarity measurement, and clone detection. The instruments used to measure similarity are semantic and structural similarity. The findings indicate opportunities for future research regarding calculating other UML diagrams, compiling calculation information for each diagram, adapting semantic and structural similarity calculation methods, determining the best weight for each item in the diagram, testing novel proposed methods, and building or finding good datasets for use as testing material

    Use and modeling of multi-agent systems in medicine

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    Multi-Agent System (MAS), and more specifically, ontology-based MAS, are increasingly being proposed and used within the medical domain. In this paper we represent an ontology-based multi-agent system specifically designed to intelligently retrieve information about human diseases. Thehuman disease ontology is organized according to the four dimensions: disease types, symptoms, causes and treatments. The multi-agent system consists of four different types of agent: Interface, Manger, Information and Smart agent. We use of UML 2.1 to model social and goal-driven nature of agents. We believe that UML 2.1 has not only provided a way for standardized notation of MAS, but also for effective representation of the dynamic processes associated with these MAS

    Integrating knowledge accross disciplines. Experiences from the NeWater project

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    The starting question for this deliverable was how to create a new adaptive management concept that can integrate insights from various disciplines and connect people from different institutional backgrounds. From literature research and empirical research on the NeWater project we identified challenges for cross-disciplinary knowledge integration, we evaluated interventions for connecting multiple knowledge frames, we analyzed the process of group model building with UML and formulated recommendations. Cross-disciplinary research has arisen from a growing number of complex problems for which knowledge of a single scientific discipline or societal field is insufficient, but presents important challenges: (1) collaboration and integration of knowledge requires in depth discussions that are timeconsuming; (2) the recursive process of problem structuring and restructuring is often at odds with the sequential planning of project activities; (3) participation and mutual learning are crucial but need to be carefully structured and sequenced; and (4) management and leadership faces the difficult challenge of balancing in depth exploration with timely delivery of tangible results. We conclude with the following general recommendations for large cross-disciplinary projects: (1) including a preparatory proposal phase for thorough exploration of opportunities of between researchers and stakeholders (2) flexible funding, planning and operational arrangements to allow for a recursive research process; (3) a project size that allows frequent interaction opportunities between researchers and between researchers and stakeholders to allow for mutual learning and in depth exploration; and (4) enhancing learning opportunities from one project to the next

    DYNASTAT: A Methodology for Dynamic and Static Modeling of Multi-agent Systems

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    Multi-agent systems are increasingly being used within various knowledge domains. The need for modeling of the multi-agent systems in a systematic and effective way is becoming more evident. In this chapter, we present the DYNASTAT methodology. This methodology involves a conceptual overview of multi-agent systems, a selection of specific agent characteristics to model, and a discussion of what has to be modeled for each of these agent characteristics. DYNASTAT is independent of any particular modeling language but provides a framework that can be used to realize a particular language in the context of a real-world example. UML 2.2 was chosen as the modeling language to implement the DYNASTAT methodology and this was illustrated using examples from the medical domain. Several UML 2.2 diagrams were selected including a use case, composite structure, sequence and activity diagram to model a multi-agent system able to assist botha medical researcher and a primary care physician. UML 2.2 provides a framework for effective modeling of agent-based systems in a standardized way which this chapter endeavors to demonstrate

    Traceability Management Architectures Supporting Total Traceability in the Context of Software Engineering

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    In the area of Software Engineering, traceability is defined as the capability to track requirements, their evolution and transformation in different components related to engineering process, as well as the management of the relationships between those components. However the current state of the art in traceability does not keep in mind many of the elements that compose a product, specially those created before requirements arise, nor the appropriated use of traceability to manage the knowledge underlying in order to be handled by other organizational or engineering processes. In this work we describe the architecture of a reference model that establishes a set of definitions, processes and models which allow a proper management of traceability and further uses of it, in a wider context than the one related to software development
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