Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11889/4529
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 | URI: | http://hdl.handle.net/20.500.11889/4529 |
Appears in Collections: | Fulltext Publications |
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176ef4196797603ae2ca68ff353bb4233668.pdf | 1.29 MB | Adobe PDF | View/Open |
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