Lazy Product Discovery in Huge Configuration Spaces

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Detalles Bibliográficos
Publicado en:arXiv.org (Mar 16, 2020), p. n/a
Autor Principal: Lienhardt, Michael
Outros autores: Damiani, Ferruccio, Johnsen, Einar Broch, Mauro, Jacopo
Publicado:
Cornell University Library, arXiv.org
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Acceso en liña:Citation/Abstract
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100 1 |a Lienhardt, Michael 
245 1 |a Lazy Product Discovery in Huge Configuration Spaces 
260 |b Cornell University Library, arXiv.org  |c Mar 16, 2020 
513 |a Working Paper 
520 3 |a 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. 
653 |a Subsystems 
653 |a Configurable programs 
653 |a Configurations 
700 1 |a Damiani, Ferruccio 
700 1 |a Johnsen, Einar Broch 
700 1 |a Mauro, Jacopo 
773 0 |t arXiv.org  |g (Mar 16, 2020), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2378468853/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2003.07383