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FAMILIAR: A domain-specific language for large scale management of feature models

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Title: FAMILIAR: A domain-specific language for large scale management of feature models
Authors: Acher, Mathieu1,2 acher@i3s.unice.fr, Collet, Philippe3, Lahire, Philippe3, France, Robert B.4
Source: Science of Computer Programming. Jun2013, Vol. 78 Issue 6, p657-681. 25p.
Subjects: Domain-specific programming languages, Computer software, Composition operators, Product lines, Reasoning, Scripting languages (Computer science)
Abstract: Abstract: The feature model formalism has become the de facto standard for managing variability in software product lines (SPLs). In practice, developing an SPL can involve modeling a large number of features representing different viewpoints, sub-systems or concerns of the software system. This activity is generally tedious and error-prone. In this article, we present FAMILIAR a Domain-Specific Language (DSL) that is dedicated to the large scale management of feature models and that complements existing tool support. The language provides a powerful support for separating concerns in feature modeling, through the provision of composition and decomposition operators, reasoning facilities and scripting capabilities with modularization mechanisms. We illustrate how an SPL consisting of medical imaging services can be practically managed using reusable FAMILIAR scripts that implement reasoning mechanisms. We also report on various usages and applications of FAMILIAR and its operators, to demonstrate their applicability to different domains and use for different purposes. [Copyright &y& Elsevier]
Copyright of Science of Computer Programming is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="JN" term="%22Science+of+Computer+Programming%22">Science of Computer Programming</searchLink>. Jun2013, Vol. 78 Issue 6, p657-681. 25p.
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  Data: Abstract: The feature model formalism has become the de facto standard for managing variability in software product lines (SPLs). In practice, developing an SPL can involve modeling a large number of features representing different viewpoints, sub-systems or concerns of the software system. This activity is generally tedious and error-prone. In this article, we present FAMILIAR a Domain-Specific Language (DSL) that is dedicated to the large scale management of feature models and that complements existing tool support. The language provides a powerful support for separating concerns in feature modeling, through the provision of composition and decomposition operators, reasoning facilities and scripting capabilities with modularization mechanisms. We illustrate how an SPL consisting of medical imaging services can be practically managed using reusable FAMILIAR scripts that implement reasoning mechanisms. We also report on various usages and applications of FAMILIAR and its operators, to demonstrate their applicability to different domains and use for different purposes. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Science of Computer Programming is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=86418107
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        Value: 10.1016/j.scico.2012.12.004
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        Text: English
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              Text: Jun2013
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