Classifying variability modeling techniques

Sinnema, Marco; Deelstra, Sybren

doi:10.1016/j.infsof.2006.08.001

Cited by 159 publications

(75 citation statements)

References 33 publications

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“…Sinnema et al [48] provide a classification of five academic (CBFM [42], COVAMOF [49], VSL [50], ConIPF [51], Koalish [52]) and one commercial (pure::variants [53]) variability modeling language. Each represents a different modeling style: CBFM, ConIPF, and pure::variants model variability as features, COVAMOF and VSL as variation points, and Koalish is embedded in the architecture description language Koala [54].…”

Section: Variability Modeling Languagesmentioning

confidence: 99%

A Study of Variability Models and Languages in the Systems Software Domain

Berger

She

Lotufo

et al. 2013

IIEEE Trans. Software Eng.

156

183

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Abstract-Variability models represent the common and variable features of products in a product line. Since the introduction of FODA in 1990, several variability modeling languages have been proposed in academia and industry, followed by hundreds of research papers on variability models and modeling. However, little is known about the practical use of such languages. We study the constructs, semantics, usage, and associated tools of two variability modeling languages, Kconfig and CDL, which are independently developed outside academia and used in large and significant software projects. We analyze 128 variability models found in twelve open source projects using these languages. Our study (1) supports variability modeling research with empirical data on the real-world use of its flagship concepts. However, we (2) also provide requirements for concepts and mechanisms that are not commonly considered in academic techniques, and (3) challenge assumptions about size and complexity of variability models made in academic papers. These results are of interest to researchers working on variability modeling and analysis techniques and to designers of tools, such as feature dependency checkers and interactive product configurators.

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Section: Variability Modeling Languagesmentioning

confidence: 99%

A Study of Variability Models and Languages in the Systems Software Domain

Berger

She

Lotufo

et al. 2013

IIEEE Trans. Software Eng.

156

183

View full text Add to dashboard Cite

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“…Invar deliberately hides the internal technical aspects of using different variability models for configuration from the stakeholders performing the configuration. The specific tools or data formats (see [3,4,5]) used for defining the variability models are not relevant for the end users who primarily focus on the available configuration choices and their implications. Invar unifies configuration operations on variability models and allows modelers to freely choose a data representation by accessing variability models through web services.…”

Section: Introductionmentioning

confidence: 99%

Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Galindo

Dhungana

Rabiser

et al. 2015

Information and Software Technology

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Context: In industrial settings products are developed by more than one organization. Software vendors and suppliers commonly typically maintain their own product lines, which contribute to a larger (multi) product line or software ecosystem. It is unrealistic to assume that the participating organizations will agree on using a specific variability modeling technique-they will rather use different approaches and tools to manage the variability of their systems. Objective: We aim to support product configuration in software ecosystems based on several variability models with different semantics that have been created using different notations. Method: We present an integrative approach that provides a unified perspective to users configuring products in multi product line environments, regardless of the different modeling methods and tools used internally. We also present a technical infrastructure and a prototype implementation based on web services. Results: We show the feasibility of the approach and its implementation by using it with the three most widespread types of variability modeling approaches in the product line community, i.e., feature-based, OVM-style, and decision-oriented modeling. To demonstrate the feasibility and flexibility of our approach, we present an example derived from industrial experience in enterprise resource planning. We further applied the approach to support the configuration of privacy settings in the Android ecosystem based on multiple variability models. We also evaluated the performance of different model enactment strategies used in our approach. Conclusions: Tools and techniques allowing stakeholders to handle variability in a uniform manner can considerably foster the initiation and growth of software ecosystems from the perspective of software reuse and configuration.

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“…Some surveys on particular variability modeling/management approaches exist, most notably on feature-oriented variability modeling [30,51,204] and on decision-oriented variability modeling [202]. Some more general surveys discuss a particular selection of approaches, e.g., [48,206].…”

Section: Variability Modeling Of Diverse Systemsmentioning

confidence: 99%

Software diversity: state of the art and perspectives

Schaefer

Rabiser²,

Clarke

et al. 2012

Int J Softw Tools Technol Transfer

136

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Abstract. Diversity is prevalent in modern software systems to facilitate adapting the software to customer requirements or the execution environment. Diversity has an impact on all phases of the software development process. Appropriate means and organizational structures are required to deal with the additional complexity introduced by software variability. This introductory article to the special section "Software Diversity -Modeling, Analysis and Evolution" provides an overview of the current state of the art in diverse systems development and discusses challenges and potential solutions. The article covers requirements analysis, design, implementation, verification and validation, maintenance and evolution as well as organizational aspects. It also provides an overview of the articles which are part of this special section and address particular issues of diverse systems development.

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Classifying variability modeling techniques

Cited by 159 publications

References 33 publications

A Study of Variability Models and Languages in the Systems Software Domain

A Study of Variability Models and Languages in the Systems Software Domain

Supporting distributed product configuration by integrating heterogeneous variability modeling approaches

Software diversity: state of the art and perspectives

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