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Title: Scalable product line configuration: A straw to break the camel's back
Authors: Sayyad, Abdel Salam
Ingram, Joseph
Menzies, Tim
Ammar, Hany
Keywords: Model-integrated computing;Computer software - Development;Artificial intelligence;Multiple criteria decision making;Mathematical optimization;Data mining;Pattern recognition systems;SMT solvers
Issue Date: 2013
Abstract: Software product lines are hard to configure. Techniques that work for medium sized product lines fail for much larger product lines such as the Linux kernel with 6000+ features. This paper presents simple heuristics that help the Indicator-Based Evolutionary Algorithm (IBEA) in finding sound and optimum configurations of very large variability models in the presence of competing objectives. We employ a combination of static and evolutionary learning of model structure, in addition to utilizing a pre-computed solution used as a “seed” in the midst of a randomly-generated initial population. The seed solution works like a single straw that is enough to break the camel’s back –given that it is a feature-rich seed. We show promising results where we can find 30 sound solutions for configuring upward of 6000 features within 30 minutes
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