Abstract
The paper questions whether data-driven and problem-driven models are sufficient for a software to automatically represent a meaningful graphical representation of scientific findings. The paper presents descriptive and prescriptive case studies to understand the benefits and the shortcomings of existing models that aim to provide graphical representations of data-sets. First, the paper considers data-sets coming from the field of software metrics and shows that existing models can provide the expected outcomes for descriptive scientific studies. Second, the paper presents data-sets coming from the field of human mobility and sustainable development, and shows that a more comprehensive model is needed in the case of prescriptive scientific fields requiring interdisciplinary research. Finally, an interdisciplinary problem-driven model is proposed to guide the software users, and specifically scientists, to produce meaningful graphical representation of research findings. The proposal is indeed based not only on a data-driven and/or problem-driven model but also on the different knowledge domains and scientific aims of the experts, who can provide the information needed for a higher-order structure of the data, supporting the graphical representation output.
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Pierro, G.A., Bergel, A., Tonelli, R., Ducasse, S. (2021). An Interdisciplinary Model for Graphical Representation. In: Cleophas, L., Massink, M. (eds) Software Engineering and Formal Methods. SEFM 2020 Collocated Workshops. SEFM 2020. Lecture Notes in Computer Science(), vol 12524. Springer, Cham. https://doi.org/10.1007/978-3-030-67220-1_12
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