Formalizing cardinality‐based feature models and their specialization

Czarnecki, Krzysztof; Helsen, Simon; Eisenecker, Ulrich W.

doi:10.1002/spip.213

Cited by 428 publications

(275 citation statements)

References 20 publications

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“…Several approaches, [3,12,15], and [35], have been proposed connecting feature modeling to formal languages. The closest work to ours is [35], where we provided a semantics for cardinalitybased feature models (a generalization of models in which we deal with feature instances) by using formal languages as the semantic domain.…”

Section: Related Work In Feature Modelingmentioning

confidence: 99%

Faithful Modeling of Product Lines with Kripke Structures and Modal Logic

Diskin¹,

Safilian²,

Maibaum³

et al. 2016

SACS

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Software product lines are now an established framework for software design. They are specified by special diagrams called feature models. For formal analysis, the latter are usually encoded by Boolean propositional theories. We discuss a major deficiency of this semantics, and show that it can be fixed by considering a product to be an instantiation process rather than its final result. We call intermediate states of this process partial products, and argue that what a feature model really defines is a poset of its partial products. We argue that such structures can be viewed as special Kripke structure that we call partial product Kripke structures, ppKS. To specify these Kripke structures, we propose a CTL-based logic, called partial product CTL, ppCTL. We show how to represent a feature model M by a ppCTL theory ML(M ) (ML stands for modal logic) such that any ppKS satisfying the theory is equal to the partial product line determined by M . Hence, ML(M ) can be considered a sound and complete representation of M . We also discuss several applications of the modal logic view in feature modeling, including refactoring of feature models.

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Section: Related Work In Feature Modelingmentioning

confidence: 99%

Faithful Modeling of Product Lines with Kripke Structures and Modal Logic

Diskin¹,

Safilian²,

Maibaum³

et al. 2016

SACS

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“…Our notation can, however, be extended to support more expressive relations between features, e.g. cardinality constraints over features [6]. The meta-model of CFMs is shown in the class diagram of Fig.…”

Section: Contextual Feature Models (Cfms)mentioning

confidence: 99%

Aligning Software Configuration with Business and IT Context

Dalpiaz

Ali

Giorgini

2012

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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Abstract. An important activity to maximize Business/IT alignment is selecting a software configuration that fits a given context. Feature models represent the space of software configurations in terms of distinguished characteristics (features). However, they fall short in representing the effect of context on the adoptability and operability of features and, thus, of configurations. Capturing this effect helps to minimize the dependency on analysts and domain experts when deriving a software for a given business and IT environment. In this paper, we propose contextual feature models as a means to explicitly represent and reason about the interplay between the variability of both features and context. We devise a formal framework and automated analyses which enable to systematically derive products aligned with an organizational context. We also propose FM-Context, a support tool for modeling and analysis.

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“…It relies upon formal studies of variability management in SPL [6,7] and in particular feature diagramming [8][9][10][11], which is a very popular notation in this community. Despite their wide acceptation, many variants have been proposed and there is no de-facto standard for feature diagrams.…”

Section: Variabilitymentioning

confidence: 99%

Weaving Variability into Domain Metamodels

Morin

Perrouin

Lahire

et al. 2009

Model Driven Engineering Languages and Systems

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Abstract. A Domain-Specific Modeling Language (DSMLs) describes the concepts and their relationships of a particular domain, in a metamodel. Using a DSML, it is possible to describe a wide range of different models. These models often share a common base and vary on some parts. Current approaches tend to distinguish the variability language from the DSMLs themselves, implying greater learning curve for DSMLs stakeholders and a significant overhead in product line engineering. We propose to consider variability as an independent aspect to be woven into the DSML to introduce variability capabilities. In particular we detail how variability is woven and how to perform product line derivation. We validate our approach through the weaving of variability into two different metamodels: Ecore and SmartAdapter, our aspect model weaver. These results emphasize how new abilities of the language can be provided by this means.

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Formalizing cardinality‐based feature models and their specialization

Cited by 428 publications

References 20 publications

Faithful Modeling of Product Lines with Kripke Structures and Modal Logic

Faithful Modeling of Product Lines with Kripke Structures and Modal Logic

Aligning Software Configuration with Business and IT Context

Weaving Variability into Domain Metamodels

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