132 research outputs found

    September 27, 1996, Ohio University Board of Trustees Meeting Minutes

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    Meeting minutes document the activities of Ohio University\u27s Board of Trustees

    Cultural Heritage on line

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    The 2nd International Conference "Cultural Heritage online – Empowering users: an active role for user communities" was held in Florence on 15-16 December 2009. It was organised by the Fondazione Rinascimento Digitale, the Italian Ministry for Cultural Heritage and Activities and the Library of Congress, through the National Digital Information Infrastructure and Preservation Program - NDIIP partners. The conference topics were related to digital libraries, digital preservation and the changing paradigms, focussing on user needs and expectations, analysing how to involve users and the cultural heritage community in creating and sharing digital resources. The sessions investigated also new organisational issues and roles, and cultural and economic limits from an international perspective

    Effects of variability in models: a family of experiments

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    The ever-growing need for customization creates a need to maintain software systems in many different variants. To avoid having to maintain different copies of the same model, developers of modeling languages and tools have recently started to provide implementation techniques for such variant-rich systems, notably variability mechanisms, which support implementing the differences between model variants. Available mechanisms either follow the annotative or the compositional paradigm, each of which have dedicated benefits and drawbacks. Currently, language and tool designers select the used variability mechanism often solely based on intuition. A better empirical understanding of the comprehension of variability mechanisms would help them in improving support for effective modeling. In this article, we present an empirical assessment of annotative and compositional variability mechanisms for three popular types of models. We report and discuss findings from a family of three experiments with 164 participants in total, in which we studied the impact of different variability mechanisms during model comprehension tasks. We experimented with three model types commonly found in modeling languages: class diagrams, state machine diagrams, and activity diagrams. We find that, in two out of three experiments, annotative technique lead to better developer performance. Use of the compositional mechanism correlated with impaired performance. For all three considered tasks, the annotative mechanism was preferred over the compositional one in all experiments. We present actionable recommendations concerning support of flexible, tasks-specific solutions, and the transfer of established best practices from the code domain to models
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