Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Galindo, José; Dhungana, Deepak; Rabiser, Rick; Benavides, David; Botterweck, Goetz; Grünbacher, Paul

doi:10.1016/j.infsof.2015.02.002

Cited by 39 publications

(22 citation statements)

References 55 publications

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“…Modelers need to explicitly define dependencies between feature models of different purpose that exist at different levels (requirement 3). This confirms the need for existing approaches for modeling dependencies between different modeling spaces [23, 37], between models of one space [31, 38] or between different levels of abstraction [62, 68]. Finally, our experiences confirm the need for mapping features to code (requirement 4).…”

Section: Exploratory Case Study: Feature Modeling Of Kemotion and Kepsupporting

confidence: 77%

Multi-purpose, multi-level feature modeling of large-scale industrial software systems

et al. 2016

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Feature models are frequently used to capture the knowledge about configurable software systems and product lines. However, feature modeling of large-scale systems is challenging as models are needed for diverse purposes. For instance, feature models can be used to reflect the perspectives of product management, technical solution architecture, or product configuration. Furthermore, models are required at different levels of granularity. Although numerous approaches and tools are available, it remains hard to define the purpose, scope, and granularity of feature models. This paper first reports results and experiences of an exploratory case study on developing feature models for two large-scale industrial automation software systems. We report results on the characteristics and modularity of the feature models, including metrics about model dependencies. Based on the findings from the study, we developed FORCE, a modeling language, and tool environment that extends an existing feature modeling approach to support models for different purposes and at multiple levels, including mappings to the code base. We demonstrate the expressiveness and extensibility of our approach by applying it to the well-known Pick and Place Unit example and an injection molding subsystem of an industrial product line. We further show how our approach supports consistency between different feature models. Our results and experiences show that considering the purpose and level of features is useful for modeling large-scale systems and that modeling dependencies between feature models is essential for developing a system-wide perspective.

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Section: Exploratory Case Study: Feature Modeling Of Kemotion and Kepsupporting

confidence: 77%

Multi-purpose, multi-level feature modeling of large-scale industrial software systems

et al. 2016

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“…Nowadays, the variability description of variability-intensive systems is getting more complex. This is done by introducing non-boolean information [145], and by using several variability models [118] in a multi-layer fashion. However, we found no proposals to reverse engineer the existing variability information of variability-intensive systems with more than one variability model or non-boolean information.…”

Section: Research Opportunitiesmentioning

confidence: 99%

“…Product configuration and derivation [35,37,42,46,47,49,50,53,55,56,57,58,59,60,65,66,68,72,74,75,76,77,80,82,83,89,93,95,98,99,100,103,105,106,107,108,109,110,112,114,116,118,119,120,123,125,126,128,133,134,135,136,…”

Section: Variability Contextmentioning

confidence: 99%

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Automated analysis of feature models: Quo vadis?

et al. 2018

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Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.

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“…Galindo et al [10] present an approach to ease the integration of variability models specified using different modeling styles, variability languages, and tools to perform configuration. They introduce the Invar approach to provide the user with a configuration tool that hides the different models, their semantics, and internal representation.…”

Section: Related Workmentioning

confidence: 99%

The High-Level Variability Language

Villota

Mazo

Salinesi

2019

Proceedings of the 23rd International Systems and Software Product Line Conference - Volume B

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Given its relevance, there is an extensive body of research for modeling variability in diverse domains. Regretfully, the community still faces issues and challenges to port or share variability models among tools and methodological approaches. There are researchers, for instance, implementing the same algorithms and analyses again because they use a specific modeling language and cannot use some existing tool. This paper introduces the High-Level Variability Language (HLVL), an expressive and extensible textual language that can be used as a modeling and an intermediate language for variability. HLVL was designed following an ontological approach, i.e., by defining their elements considering the meaning of the concepts existing on different variability languages. Our proposal not only provides a unified language based on a comprehensive analysis of the existing ones but also sets foundations to build tools that support different notations and their combination. CCS CONCEPTS• Software and its engineering → Domain specific languages; Software product lines.

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Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Cited by 39 publications

References 55 publications

Multi-purpose, multi-level feature modeling of large-scale industrial software systems

Multi-purpose, multi-level feature modeling of large-scale industrial software systems

Automated analysis of feature models: Quo vadis?

The High-Level Variability Language

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