Article

Sample Spaces and Feature Models: There and Back Again

Authors:

Krzysztof Czarnecki,

Steven She,

Andrzej WasowskiAuthors Info & Claims

SPLC '08: Proceedings of the 2008 12th International Software Product Line Conference

Pages 22 - 31

https://doi.org/10.1109/SPLC.2008.49

Published: 08 September 2008 Publication History

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Abstract

We present probabilistic feature models (PFMs) and illustrate their use by discussing modeling, mining and interactive configuration. PFMs are formalized as a set of formulas in a certain probabilistic logic. Such formulas can express both hard and soft constraints and have a well defined semantics by denoting a set of joint probability distributions over features. We show how PFMs can be mined from a given set of feature configurations using data mining techniques. Finally, we demonstrate how PFMs can be used in configuration in order to provide automated support for choice propagation based on both hard and soft constraints. We believe that these results constitute solid foundations for the construction of reverse engineering tools for software product lines and configurators using soft constraints.

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Horcas JBurgueño LKienzle J(2025)Leveraging belief uncertainty for informed decision making in software product line evolutionJournal of Systems and Software10.1016/j.jss.2024.112235219:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.jss.2024.112235
Malenfant JZiadi T(2024)Can Conditional Preferences and *CP-net Concepts Enhance Feature Models?Proceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3676552(66-74)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3676552
Horcas JGalindo JHeradio RFernandez-Amoros DBenavides D(2023)A Monte Carlo tree search conceptual framework for feature model analysesJournal of Systems and Software10.1016/j.jss.2022.111551195:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.jss.2022.111551
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Sample Spaces and Feature Models: There and Back Again
1. Information systems
  1. Information systems applications

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SPLC '08: Proceedings of the 2008 12th International Software Product Line Conference

September 2008

330 pages

ISBN:9780769533032

Publisher

IEEE Computer Society

United States

Publication History

Published: 08 September 2008

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Horcas JBurgueño LKienzle J(2025)Leveraging belief uncertainty for informed decision making in software product line evolutionJournal of Systems and Software10.1016/j.jss.2024.112235219:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.jss.2024.112235
Malenfant JZiadi T(2024)Can Conditional Preferences and *CP-net Concepts Enhance Feature Models?Proceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3676552(66-74)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3676552
Horcas JGalindo JHeradio RFernandez-Amoros DBenavides D(2023)A Monte Carlo tree search conceptual framework for feature model analysesJournal of Systems and Software10.1016/j.jss.2022.111551195:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.jss.2022.111551
Horcas JGalindo JHeradio RFernandez-Amoros DBenavides DMousavi MSchobbens P(2021)Monte Carlo tree search for feature model analysesProceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A10.1145/3461001.3471146(190-201)Online publication date: 6-Sep-2021
https://dl.acm.org/doi/10.1145/3461001.3471146
ter Beek MLegay ALluch Lafuente AVandin A(2021)Quantitative Security Risk Modeling and Analysis with RisQFLanComputers and Security10.1016/j.cose.2021.102381109:COnline publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1016/j.cose.2021.102381
Heradio RFernandez-Amoros DMayr-Dorn CEgyed AAtlee JBultan TWhittle J(2019)Supporting the statistical analysis of variability modelsProceedings of the 41st International Conference on Software Engineering10.1109/ICSE.2019.00091(843-853)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1109/ICSE.2019.00091
Gonzalez-Fernandez YHamidi SChen SLiaskos S(2019)Efficient elicitation of software configurations using crowd preferences and domain knowledgeAutomated Software Engineering10.1007/s10515-018-0247-426:1(87-123)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s10515-018-0247-4
El-Sharkawy SDhar SKrafczyk ADuszynski SBeichter TSchmid K(2018)Reverse engineering variability in an industrial product lineProceedings of the 22nd International Systems and Software Product Line Conference - Volume 110.1145/3233027.3233047(215-225)Online publication date: 10-Sep-2018
https://dl.acm.org/doi/10.1145/3233027.3233047
Kaindl HKramer SHoch R(2018)An inductive learning perspective on automated generation of feature models from given product specificationsProceedings of the 22nd International Systems and Software Product Line Conference - Volume 110.1145/3233027.3233031(25-30)Online publication date: 10-Sep-2018
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Pereira JMartinez JGurudu HKrieter SSaake GHaddad HWainwright RChbeir R(2018)Visual guidance for product line configuration using recommendations and non-functional propertiesProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167353(2058-2065)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167353
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Lazy product discovery in huge configuration spaces

On extracting feature models from sets of valid feature combinations

A genetic algorithm for optimized feature selection with resource constraints in software product lines

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