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Extraction of feature models from formal contexts

Published: 21 August 2011 Publication History

Abstract

For economical reasons, the creation of feature oriented software should include previously created products and should not be done from scratch. To speed up this migration process, feature models have to be generated automatically from existing product variants. This work presents an approach based on formal concept analysis that analyzes incidence matrices containing matching relations as input and creates feature models as output. The resulting feature models describe exactly the given input variants. The introduced novel optimized approach performs this transformation in reasonable time even for large product libraries.

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Published In

cover image ACM Other conferences
SPLC '11: Proceedings of the 15th International Software Product Line Conference, Volume 2
August 2011
306 pages
ISBN:9781450307895
DOI:10.1145/2019136
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • Pure-Systems: Pure-Systems GmbH
  • Hitachi
  • Siemens
  • Software Eng Inst: Software Engineering Institute
  • Biglever: BigLever Software, Inc.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2011

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Author Tags

  1. feature models
  2. formal concept analysis

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  • Research-article

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SPLC '11
Sponsor:
  • Pure-Systems
  • Software Eng Inst
  • Biglever
SPLC '11: The 2011 Software Product Line Conference
August 21 - 26, 2011
Munich, Germany

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Overall Acceptance Rate 167 of 463 submissions, 36%

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Cited By

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  • (2024)Taming the Variability of Browser FingerprintsProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3672591(66-71)Online publication date: 2-Sep-2024
  • (2024)Exploring the 3-Dimensional Variability of Websites' User-Stories using Triadic Concept AnalysisInternational Journal of Approximate Reasoning10.1016/j.ijar.2024.109248(109248)Online publication date: Jul-2024
  • (2023)Towards Analyzing Variability in Space and Time of Products from a Product Line using Triadic Concept AnalysisProceedings of the 27th ACM International Systems and Software Product Line Conference - Volume B10.1145/3579028.3609019(85-89)Online publication date: 28-Aug-2023
  • (2023)Guiding Feature Models Synthesis from User-Stories: An Exploratory ApproachProceedings of the 17th International Working Conference on Variability Modelling of Software-Intensive Systems10.1145/3571788.3571797(65-70)Online publication date: 25-Jan-2023
  • (2022)Adaptive behavioral model learning for software product linesProceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A10.1145/3546932.3546991(142-153)Online publication date: 12-Sep-2022
  • (2022)Machine Learning for Feature Constraints DiscoveryHandbook of Re-Engineering Software Intensive Systems into Software Product Lines10.1007/978-3-031-11686-5_7(175-196)Online publication date: 5-Jul-2022
  • (2022)Extending Boolean Variability Relationship Extraction to Multi-valued Software DescriptionsHandbook of Re-Engineering Software Intensive Systems into Software Product Lines10.1007/978-3-031-11686-5_6(143-173)Online publication date: 23-Nov-2022
  • (2022)Bottom-Up Technologies for Reuse: A Framework to Support Extractive Software Product Line Adoption ActivitiesHandbook of Re-Engineering Software Intensive Systems into Software Product Lines10.1007/978-3-031-11686-5_14(355-377)Online publication date: 5-Jul-2022
  • (2021)Exploring Variability of Visual Accessibility Options in Operating SystemsFuture Internet10.3390/fi1309023013:9(230)Online publication date: 4-Sep-2021
  • (2021)Learning by sampling: learning behavioral family models from software product linesEmpirical Software Engineering10.1007/s10664-020-09912-w26:1Online publication date: 1-Jan-2021
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