Proceedings of the Eighth International Workshop on Variability Modelling of Software-Intensive Systems 2014
DOI: 10.1145/2556624.2556641
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Towards system analysis with variability model metrics

Abstract: Variability models are central artifacts in highly configurable systems. They aim at planning, developing, and configuring systems by describing configuration knowledge at different levels of formality. The existence of large models using a variety of modeling concepts in heterogeneous languages with intricate semantics calls for a unified measuring approach. In this position paper, we attempt to take a first step towards such a measurement. We discuss perspectives of metrics, define low-level measurement goal… Show more

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Cited by 23 publications
(14 citation statements)
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“…Model generation (a.k.a., instance generation) automatically creates instances (models) of the language, typically aiming at instances with certain properties, such as size, coverage of language concepts, or other structural characteristics (e.g., cross-tree constraints ratio [8,40,50]). Tool developers can use it to generate a set of models, useful for functional testing and performance testing of the different tools supporting the language.…”
Section: David Benavides José Galindomentioning
confidence: 99%
“…Model generation (a.k.a., instance generation) automatically creates instances (models) of the language, typically aiming at instances with certain properties, such as size, coverage of language concepts, or other structural characteristics (e.g., cross-tree constraints ratio [8,40,50]). Tool developers can use it to generate a set of models, useful for functional testing and performance testing of the different tools supporting the language.…”
Section: David Benavides José Galindomentioning
confidence: 99%
“…In this paper we use some of those metrics for evaluating the feature model of the system, namely: number of features (NoF) [9], coefficient of conectivity-density (CoC) [4], depth of tree (DoT) [6,9], number of leaf nodes (NLeaf) [9], number of Or-feature groups [7], number of X or-feature groups [7], the ratio of variability (RoV) [4], and the theoretical (de-)selection ration (TSR) [17]. Section 4 presents interesting findings from this evaluation.…”
Section: Measures For Software Product Lines and Variability Managementmentioning
confidence: 99%
“…In this section we present an empirical evaluation of the system under a set of metrics for variability obtained from [21]. More specifically, we apply the following metrics on the feature model: number of features (NoF) [9], coefficient of connectivity-density (CoC) [4], depth of tree (DoT) [6,9], number of leaf nodes (NLeaf) [9], number of Or-feature groups [7], number of Xor-feature groups [7], the ratio of variability (RoV) [4], and the theoretical (de-)selection ration (TSR) [17]. These metrics give us information about the feature model of the system and, more importantly, about the use of the features of the system in real deployments.…”
Section: Empirical Evaluationmentioning
confidence: 99%
“…Evaluating the maturity (or, performance) of SPLE in an organization is challenging. Various measurement techniques have been proposed, ranging from cost and decision models targeting SPLE (Ali et al 2009;Khurum et al 2008;Krüger 2016;Thummalapenta et al 2010) to metrics used to measure the product lines themselves (El-Sharkawy et al 2019;Montagud et al 2012;Berger and Guo 2014). A prominent evaluation technique for SPLE is the Family Evaluation Framework (FEF), assessing the performance of SPLE (not to be confused with the performance of the product line) in an organization along the four BAPO concerns (van der Linden et al 2004;Pohl et al 2005;.…”
Section: Introductionmentioning
confidence: 99%