A systematic analysis of textual variability modeling languages

Eichelberger, Holger; Schmid, Klaus

doi:10.1145/2491627.2491652

Cited by 41 publications

(21 citation statements)

References 30 publications

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“…For instance, feature models have become more and more sophisticated since 1990 and their dialects have been detailed in comprehensive surveys, for example, by Schobbens et al [25], Benavides et al [4] and Eichelberger and Schmid [13]. Boolean constructs of feature models (as supported by FODA [17], FDL [28], XSFM [19], or Velvet [23]) are useful in the video domain, but not sufficient.…”

Section: Comparison With Existing Solutionsmentioning

confidence: 99%

Modeling variability in the video domain: language and experience report

et al. 2018

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This paper reports about a new domain-specific variability modeling language, called VM, resulting from the close collaboration with industrial partners in the video domain. We expose the requirements and advanced variability constructs required to characterize and realize variations of physical properties of a video (such as objects' speed or scene illumination). The results of our experiments and industrial experience show that VM is effective to model complex variability information and can be exploited to synthesize video variants. We concluded that basic variability mechanisms are useful but not enough, attributes and multi-features are of prior importance, and meta-information is relevant for efficient variability analysis. In addition, we questioned the existence of one-size-fits-all variability modeling solution applicable in any industry. Yet, some common needs for modeling variability are becoming apparent such as support for attributes and multi-features. Mots-clés : VM, modélisation de la variabilité, ingénierie des lignes de produits, systèmes hautement configurables, langage textuel de spécification

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Section: Comparison With Existing Solutionsmentioning

confidence: 99%

Modeling variability in the video domain: language and experience report

et al. 2018

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“…However, in our case, our EC2 FM is large and complex enough to use a text format. There are different alternatives for FM text formats [28], but only a few of them provide automated analysis support. Since we take advantage of the analysis operations already provided by FaMa, we use the FaMa notation to define the EC2 FM in plain text.…”

Section: Ec2 Fmmentioning

confidence: 99%

Automated configuration support for infrastructure migration to the cloud

García-Galán

Trinidad

Rana

et al. 2016

Future Generation Computer Systems

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h i g h l i g h t s• We support the decision making in migration planning to the cloud.• We use Feature Models to describe the configuration space of an IaaS. • We automate the search of the most suitable IaaS configuration. • Our approach improves the results of commercial applications on Amazon EC2. a b s t r a c tWith an increasing number of cloud computing offerings in the market, migrating an existing computational infrastructure to the cloud requires comparison of different offers in order to find the most suitable configuration. Cloud providers offer many configuration options, such as location, purchasing mode, redundancy, and extra storage. Often, the information about such options is not well organised. This leads to large and unstructured configuration spaces, and turns the comparison into a tedious, errorprone search problem for the customers. In this work we focus on supporting customer decision making for selecting the most suitable cloud configuration-in terms of infrastructural requirements and cost. We achieve this by means of variability modelling and analysis techniques. Firstly, we structure the configuration space of an IaaS using feature models, usually employed for the modelling of variabilityintensive systems, and present the case study of the Amazon EC2. Secondly, we assist the configuration search process. Feature models enable the use of different analysis operations that, among others, automate the search of optimal configurations. Results of our analysis show how our approach, with a negligible analysis time, outperforms commercial approaches in terms of expressiveness and accuracy.

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“…Furthermore, they described some modeling tools such as MetaCASE, Ami Eddi, and ConfigEditor, which do not provide a full support for the reasoning on extended FM s. Other approaches such as Clafer , VELVET or TVL only partially support constraints on attributes values. For instance, assigning a value to an attribute is not fully supported by these variability modeling approaches , while SALOON does provide such a support. Authors also proposed approaches regarding constraints with cardinalities.…”

Section: Related Workmentioning

confidence: 99%

SALOON: a platform for selecting and configuring cloud environments

2015

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International audienceMigrating legacy systems or deploying a new application to a cloud environment has recently become very trendy, because the number of cloud providers available is still increasing. These cloud environments provide a wide range of resources at different levels of functionality, which must be appropriately configured by stakeholders for the application to run properly. Handling this variability during the configuration and deployment stages is known as a complex and error-prone process, usually made in an ad hoc manner. In this paper, we propose SALOON, a software product lines-based platform to face these issues. We describe the architecture of the SALOON platform, which relies on feature models combined with a domain model used to select among cloud environments a well-suited one. SALOON supports stakeholders while configuring the selected cloud environment in a consistent way and automates the deployment of such configurations through the generation of executable configuration scripts. This paper also reports on some experiments, showing that using SALOON significantly reduces time to configure a cloud environment compared with a manual approach and provides a reliable way to find a correct and suitable configuration. Moreover, our empirical evaluation shows that our approach is effective and scalable to properly deal with a significant number of cloud environments

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A systematic analysis of textual variability modeling languages

Cited by 41 publications

References 30 publications

Modeling variability in the video domain: language and experience report

Modeling variability in the video domain: language and experience report

Automated configuration support for infrastructure migration to the cloud

SALOON: a platform for selecting and configuring cloud environments

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