Automatic Generation of Search-Based Algorithms Applied to the Feature Testing of Software Product Lines

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Filho:2017:AGS:3131151.3131152,

• author = "Helson L. {Jakubovski Filho} and Jackson A. {Prado Lima} and Silvia R. Vergilio",
• title = "Automatic Generation of Search-Based Algorithms Applied to the Feature Testing of Software Product Lines",
• booktitle = "Proceedings of the 31st Brazilian Symposium on Software Engineering, SBES-2017",
• year = "2017",
• editor = "Jose Carlos Maldonado and Fabiano {Cutigi Ferrari} and Uira Kulesza and Tayana {Uchoa Conte}",
• pages = "114--123",
• address = "Fortaleza, CE, Brazil",
• month = sep # " 20-22",
• organisation = "Brazilian Computer Society",
• publisher = "ACM",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, NSGA-II, SBSE, SPL, Hyper-Heuristics, Search-Based Software Engineering, Software Product Line Testing",
• isbn13 = "978-1-4503-5326-7",
• acmid = "3131152",
• DOI = "doi:10.1145/3131151.3131152",
• size = "10 pages",
• abstract = "The selection of products for the variability testing of Feature Models (FMs) is a complex task impacted by many factors. To solve this problem, Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully used in the field known as Search-Based Software Engineering (SBSE). However, the design of a search-based approach is not an easy task for the software engineer, who can find some difficulties such as: the choice and configuration of the best MOEAs, the choice of the best search operators to be implemented, and so on. In addition to this, existing approaches are dependent on the problem domain and do not allow reuse. In this way the use of Hyper-Heuristic (HH) can help to obtain more generic and reusable search-based approaches, and because of this is considered a trend in the SBSE field. Following this trend and to contribute to reduce the software engineer's efforts, this work explores the use of a hyper-heuristic for automatic generation of MOEAs to select test products from the FM",
• notes = "http://www.lia.ufc.br/~cbsoft2017/en/xxxi-sbes/sbes-cfp/

  Department of Computer Science, Federal University of Parana, Curitiba, Parana, Brazil",

}

Genetic Programming entries for Helson L Jakubovski Filho Jackson A Prado Lima Silvia Regina Vergilio

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