Using multiple feature models to specify configuration options for electrical transformers

Chavarriaga, Jaime; Rangel, C.; Noguera, Carlos; Casallas, Rubby; Jonckers, Viviane

doi:10.1145/2791060.2791091

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…For example, these works do not report on combining bottom-up and top-down modeling, model hierarchy definitions, and model views. Several industrial case-studies reported practices used for creating feature models with the purpose to adopt product lines [19,34,38,59]. Because these case studies report experiences from specific domains and are based on specific technologies, the reported practices are rather specific.…”

Section: Related Workmentioning

confidence: 99%

Principles of feature modeling

Nešić

Krüger

Stănciulescu

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Feature models are arguably one of the most intuitive and successful notations for modeling the features of a variant-rich software system. Feature models help developers to keep an overall understanding of the system, and also support scoping, planning, development, variant derivation, configuration, and maintenance activities that sustain the system's long-term success. Unfortunately, feature models are difficult to build and evolve. Features need to be identified, grouped, organized in a hierarchy, and mapped to software assets. Also, dependencies between features need to be declared. While feature models have been the subject of three decades of research, resulting in many feature-modeling notations together with automated analysis and configuration techniques, a generic set of principles for engineering feature models is still missing. It is not even clear whether feature models could be engineered using recurrent principles. Our work shows that such principles in fact exist. We analyzed feature-modeling practices elicited from ten interviews conducted with industrial practitioners and from 31 relevant papers. We synthesized a set of 34 principles covering eight different phases of feature modeling, from planning over model construction, to model maintenance and evolution. Grounded in empirical evidence, these principles provide practical, contextspecific advice on how to perform feature modeling, describe what information sources to consider, and highlight common characteristics of feature models. We believe that our principles can support researchers and practitioners enhancing feature-modeling tooling, synthesis, and analyses techniques, as well as scope future research.

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Section: Related Workmentioning

confidence: 99%

Principles of feature modeling

Nešić

Krüger

Stănciulescu

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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“…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%

“…Experience Report [53,64,76,95,108,242] 6 Table 9: Classification of papers based on the research facet 5.5 RQ5: When have the papers been published?…”

Section: Research Facetmentioning

confidence: 99%

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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