Supporting the Statistical Analysis of Variability Models

Heradio, Rubén, Fernández Amoros, David, Mayr Dorn, Christoph y Egyed, Alexander(2019) .Supporting the Statistical Analysis of Variability Models. IEEE/ACM 41st International Conference on Software Engineering (ICSE).En: Montreal (Canada). (2019-05-25)

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Título de la Conferencia IEEE/ACM 41st International Conference on Software Engineering (ICSE)
Fecha de inicio de la Conferencia 2019-05-25
Fecha fín de la Conferencia 2019-05-31
Lugar de la Conferencia Montreal (Canada)
Fecha de presentación de la Ponencia 2019
Numeros de las páginas 843-853
Titulo Supporting the Statistical Analysis of Variability Models
Autor(es) Heradio, Rubén
Fernández Amoros, David
Mayr Dorn, Christoph
Egyed, Alexander
Notas adicionales This is an Accepted Manuscript of an article published by IEEE in "IEEE/ACM 41st International Conference on Software Engineering (ICSE), Montreal, QC, Canada, 2019, pp. 843-853", available at: https://doi.org/10.1109/ICSE.2019.00091 Este es el manuscrito aceptado del artículo publicado por IEEE en "IEEE/ACM 41st International Conference on Software Engineering (ICSE), Montreal, QC, Canada, 2019, pp. 843-853", disponible en línea: https://doi.org/10.1109/ICSE.2019.00091 https://doi.org/10.1109/ICSE.2019.00091
Materia(s) Ingeniería Informática
Abstract Variability models are broadly used to specify the configurable features of highly customizable software. In practice, they can be large, defining thousands of features with their dependencies and conflicts. In such cases, visualization techniques and automated analysis support are crucial for understanding the models. This paper contributes to this line of research by presenting a novel, probabilistic foundation for statistical reasoning about variability models. Our approach not only provides a new way to visualize, describe and interpret variability models, but it also supports the improvement of additional state-of-the-art methods for software product lines; for instance, providing exact computations where only approximations were available before, and increasing the sensitivity of existing analysis operations for variability models. We demonstrate the benefits of our approach using real case studies with up to 17,365 features, and written in two different languages (KConfig and feature models).
Palabra clave Variability modeling
feature modeling
software product lines
software visualization
binary decision diagrams
Editor(es) Institute of Electrical and Electronics Engineers (IEEE)
Fecha 2019-08-26
Formato application/pdf
Identificador bibliuned:DptoISSI-ETSI-Ponencias-Rheradio-0001
http://e-spacio.uned.es/fez/view/bibliuned:DptoISSI-ETSI-Ponencias-Rheradio-0001
https://doi.org/10.1109/ICSE.2019.00091
Total de paginas 11
Idioma eng
Versión de la publicación submittedVersion
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de recurso conferenceObject
Tipo de acceso Acceso abierto

 
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Creado: Mon, 06 May 2024, 18:24:33 CET