Composing Your Compositions of Variability Models

Acher, Mathieu; Combemale, Benôıt; Collet, Philippe; Barais, Olivier; Lahire, Philippe

doi:10.1007/978-3-642-41533-3_22

Cited by 20 publications

(18 citation statements)

References 38 publications

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“…WebFML is built on top of FAMILIAR [42] and provides FM management operations all based on the synthesis procedure. Likewise practitioners can envision the use of WebFML in practical scenarios: the merging of multiple product lines [7]; the slicing of a configuration process into different steps or tasks [51]; the sound refactoring of FMs [8], especially when fully automated techniques produce incorrect FMs; the reverse engineering of configurable systems through the combined use of composition, decomposition and refactoring operators [2,3].…”

Section: Tool Supportmentioning

confidence: 99%

Breathing ontological knowledge into feature model synthesis: an empirical study

et al. 2015

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Feature Models (FMs) are a popular formalism for modeling and reasoning about the configurations of a software product line. As the manual construction of an FM is time-consuming and error-prone, management operations have been developed for reverse engineering, merging, slicing, or refactoring FMs from a set of configurations/dependencies. Yet the synthesis of meaningless ontological relations in the FM-as defined by its feature hierarchy and feature groups-may arise and cause severe difficulties when reading, maintaining or exploiting it. Numerous synthesis techniques and tools have been proposed, but only a few consider both configuration and ontological semantics of an FM. There are also few empirical studies investigating ontological aspects when synthesizing FMs. In this article, we define a generic, ontologic-aware synthesis procedure that computes the likely siblings or parent candidates for a given feature. We develop six heuristics for clustering and weighting the logical, syntactical and semantical relationships between feature names. We then perform an empirical evaluation on hundreds of FMs, coming from the SPLOT repository and Wikipedia. We provide evidence that a fully automated synthesis (i.e., without any user intervention) is likely to produce FMs far from the ground truths. As the role of the user is crucial, we empirically analyze the strengths and weaknesses of heuristics for computing ranking lists and different kinds of clusters. We show that a hybrid approach mixing logical and ontological techniques outperforms state-of-the-art solutions. We believe our approach, environment, and empirical results support researchers and practitioners working on reverse engineering and management of FMs.

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Section: Tool Supportmentioning

confidence: 99%

Breathing ontological knowledge into feature model synthesis: an empirical study

et al. 2015

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“…Another important application of the hierarchical semantics of CFDs regards feature model management, which is an active area in feature modeling. By feature model management, we mean feature model composition via some operations like merging, intersection, and union, etc [25,1,2]. Characterization of the hierarchical semantics come in handy here.…”

Section: Other Practical Applicationsmentioning

confidence: 99%

Hierarchical Multiset Theories of Cardinality-Based Feature Diagrams

Safilian

Maibaum

2016

2016 10th International Symposium on Theoretical Aspects of Software Engineering (TASE)

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Software product line engineering is a very common method for designing complex software systems. Feature modeling is the most common approach to specify product lines. The main part of a feature model is a special tree of features called a feature diagram. Cardinality-based feature diagrams provide the most expressive tool among the current feature diagram languages. The most common characterization of the semantics of a cardinality-based diagram is the set of flat multisets over features satisfying the constraints. However, this semantics provides a poor abstract view of the diagram. We address this problem by proposing another multiset theory for the cardinality-based feature diagram, called the hierarchical theory of the diagram. We show that this semantics captures all information of the diagram so that one can retrieve the diagram from its hierarchical semantics. We also characterize sets of multisets, which can provide a hierarchical semantics of some diagrams.

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“…To do so, it relies on the synthesis algorithm from [36] to build back a hierarchy. Recently, new forms of composition have been explored with differences in the expressed configurations and the ontological semantics [26]. Two new implementations have been devised and implemented, one relying on the slice operator, the other one using a local synthesis approach.…”

Section: Semantic Propertiesmentioning

confidence: 99%

“…The recent implementation of some variants of the merge operation is an example [26], but this is actually the case in the kernel of the DSL and in all its interaction points (extraction of variability, internal representation, relations to other models).…”

Section: Challenges Aheadmentioning

confidence: 99%

“…The language has been used in various case studies [23][24][25], ranging from forward to reverse engineering, with different domains and varied stakeholders. It has also evolved with extended merging techniques [26], better reverse engineering mechanisms [27], but also an additional Java API and a new implementation as an internal DSL in Scala.…”

Section: Introductionmentioning

confidence: 99%

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Domain Specific Languages for Managing Feature Models: Advances and Challenges

Collet

2014

Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change

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Abstract. Managing multiple and complex feature models is a tedious and error-prone activity in software product line engineering. Despite many advances in formal methods and analysis techniques, the supporting tools and APIs are not easily usable together, nor unified. In this paper, we report on the development and evolution of the Familiar Domain-Specific Language (DSL). Its toolset is dedicated to the large scale management of feature models through a good support for separating concerns, composing feature models and scripting manipulations. We overview various applications of Familiar and discuss both advantages and identified drawbacks. We then devise salient challenges to improve such DSL support in the near future.

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Composing Your Compositions of Variability Models

Cited by 20 publications

References 38 publications

Breathing ontological knowledge into feature model synthesis: an empirical study

Breathing ontological knowledge into feature model synthesis: an empirical study

Hierarchical Multiset Theories of Cardinality-Based Feature Diagrams

Domain Specific Languages for Managing Feature Models: Advances and Challenges

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