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Preprint Open Access

Defining Categorical Reasoning of Numerical Feature Models with Feature-Wise and Variant-Wise Quality Attributes

Daniel-Jesus Munoz; Monica Pinto; Dilian Gurov; Lidia Fuentes

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Automatic analysis of variability is an important stage of Software Product Line (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model.

In this paper, we make use of the Quality Variability Model (QVM), based on Category Theory (CT), to redefine reasoning operations. We start defining and composing the six most common operations in SPL, but now as quality-based queries, which tend to be unavailable in other approaches. Consequently, QVM supports interactions between variant-wise and feature-wise quality attributes. As a proof of concept, we present, implement and execute the operations as lambda reasoning for CQL IDE -- the state-of-the-art CT tool.

Munoz, Pinto and Fuentes work is supported by the European Union's H2020 research and innovation programme under grant agreement DAEMON 101017109, by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 and Rhea P18-FR-1081 and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación.
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