Lazy product discovery in huge configuration spaces

Michael Lienhardt, Ferruccio Damiani, Einar Broch Johnsen, Jacopo Mauro

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Highly-configurable software systems can have thousands of interdependent configuration options across different subsystems. In the resulting configuration space, discovering a valid product configuration for some selected options can be complex and error prone. The configuration space can be organized using a feature model, fragmented into smaller interdependent feature models reflecting the configuration options of each subsystem. We propose a method for lazy product discovery in large fragmented feature models with interdependent features. We formalize the method and prove its soundness and completeness. The evaluation explores an industrial-size configuration space. The results show that lazy product discovery has significant performance benefits compared to standard product discovery, which in contrast to our method requires all fragments to be composed to analyze the feature model. Furthermore, the method succeeds when more efficient, heuristics-based engines fail to find a valid configuration.

Original languageEnglish
Title of host publicationProceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering, ICSE 2020
PublisherAssociation for Computing Machinery
Publication date27. Jun 2020
Pages1509-1521
Article number3380372
ISBN (Electronic)9781450371216
DOIs
Publication statusPublished - 27. Jun 2020
Event42nd ACM/IEEE International Conference on Software Engineering, ICSE 2020 - Virtual, Online, Korea, Republic of
Duration: 27. Jun 202019. Jul 2020

Conference

Conference42nd ACM/IEEE International Conference on Software Engineering, ICSE 2020
Country/TerritoryKorea, Republic of
CityVirtual, Online
Period27/06/202019/07/2020
SponsorACM Special Interest Group on Software Engineering (SIGSOFT), IEEE Computer Society Technical Council on Software Engineering (TCSE), Korean Institute of Information Scientists and Engineers (KIISE)

Keywords

  • Composition
  • Configurable software
  • Feature models
  • Linux distribution
  • Software product lines
  • Variability modeling

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