Abstract
Configuring a SPL is a cognitively difficult activity that requires a deep understanding of the features and their constraints to be performed effectively. To this end, SPL configurators have been equipped with various visualizations to assist users in their tasks. However, there are many ways to visualize data: the process of associating an efficient visualization to a given (configuration) task is neither well-understood nor systematically applied, resulting in confusing visualizations yielding configuration errors. In this chapter, we offer such a process, based on theories of the visualization community for data representation. The first step consists in choosing the data to be visualized. This selection induces restrictions on the types of visualization that are then computed based on the data characteristics and best practices from semiology and visual languages. Designers can then select an efficient visualization for the intended task. Our process is supported by feature models and FAMILIAR to merge and constrain the set of applicable visualizations.
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This work was partly supported by the European Regional Development Fund (ERDF IDEES/CO-INNOVATION).
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Sauvage-Thomase, C., Biri, N., Perrouin, G., Genon, N., Heymans, P. (2017). Feature-Based Elicitation of Cognitively Efficient Visualizations for SPL Configurations. In: Sottet, JS., García Frey, A., Vanderdonckt, J. (eds) Human Centered Software Product Lines. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-60947-8_4
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DOI: https://doi.org/10.1007/978-3-319-60947-8_4
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