Feature Model Synthesis with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Lukas Linsbauer and Roberto Erick Lopez-Herrejon and 
                 Alexander Egyed",
  title =        "Feature Model Synthesis with Genetic Programming",
  booktitle =    "Proceedings of the 6th International Symposium, on
                 Search-Based Software Engineering, SSBSE 2014",
  year =         "2014",
  editor =       "Claire {Le Goues} and Shin Yoo",
  volume =       "8636",
  series =       "LNCS",
  pages =        "153--167",
  address =      "Fortaleza, Brazil",
  month =        "26-29 " # aug,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, SBSE, Feature
                 Models, Feature Set, Reverse Engineering, Software
                 Product Lines, Variability Modelling",
  isbn13 =       "978-3-319-09939-2",
  URL =          "http://www.springer.com/computer/swe/book/978-3-319-09939-2",
  DOI =          "doi:10.1007/978-3-319-09940-8_11",
  size =         "15 pages",
  abstract =     "Search-Based Software Engineering (SBSE) is successful
                 on several stages of the software development life
                 cycle. It has also been applied to different challenges
                 in the context of Software Product Lines (SPLs) like
                 generating minimal test suites. When reverse
                 engineering SPLs from legacy software an important
                 challenge is the reverse engineering of variability,
                 often expressed in the form of Feature Models (FMs).
                 The synthesis of FMs has been studied with techniques
                 such as Genetic Algorithms. In this paper we explore
                 the use of Genetic Programming for this task. We sketch
                 our general workflow, the GP pipeline employed, and its
                 evolutionary operators. We report our experience in
                 synthesising feature models from sets of feature
                 combinations for 17 representative feature models, and
                 analyse the results using standard information
                 retrieval metrics.",

Genetic Programming entries for Lukas Linsbauer Roberto E Lopez-Herrejon Alexander Egyed