Recognizing specific characteristics of feature models (FM) can be challenging due to the different nature and domains of the models. There are several metrics to characterize FMs. However, there is no standard way to visualize and identify the properties that make an FM unique and distinguishable. We propose FM Fact Label as a tool to visualize an FM characterization based on its metadata, structural measures, and analytical metrics. Although existing tools can provide a visualization of the FM and report some metrics, the feature diagram of large-scale FMs becomes ineffective to take an overall shape of the FM properties. Moreover, the reported metrics are often embedded in the tool user interface, preventing further analysis. FM Fact Label is a standalone web-based tool that provides a configurable and interactive visualization of FM characterizations that can be exported to several formats. Our contribution becomes important because the Universal Variability Language (UVL) is starting to gain attraction in the software product line community as a unified textual language to specify FMs and share knowledge. With this contribution, we help to advance the UVL ecosystem one step forward while providing a common representation for the results of existing analysis tools.

Work supported by the projects OPHELIA (RTI2018-101204-B-C22), COPERNICA (P20_01224), METAMORFOSIS (FEDER_US-1381375), MEDEA (RTI2018-099213-B-I00), IRIS (PID2021-122812OB-I00), Rhea (P18-FR-1081), LEIA (UMA18-FEDERIA-157), and DAEMON (H2020-101017109), and Juan de la Cierva—Formación 2019 grant.