Empirical Evaluation of Conditional Operators in GP Based Fault Localization

Created by W.Langdon from gp-bibliography.bib Revision:1.4009

@InProceedings{Kang:2017:GECCO,
  author =       "Dahyun Kang and Jeongju Sohn and Shin Yoo",
  title =        "Empirical Evaluation of Conditional Operators in {GP}
                 Based Fault Localization",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "1295--1302",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071263",
  DOI =          "doi:10.1145/3071178.3071263",
  acmid =        "3071263",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, fault
                 localization",
  month =        "15-19 " # jul,
  abstract =     "Genetic Programming has been successfully applied to
                 learn to rank program elements according to their
                 likelihood of containing faults. However, all
                 GP-evolved formula that have been studied in the fault
                 localization literature up to now are single
                 expressions that only use a small set of basic
                 functions. Based on recent theoretical analysis that
                 different formulae may be more effective against
                 different classes of faults, we evaluate the impact of
                 allowing ternary conditional operators in GP-evolved
                 fault localization by extending our fault localization
                 tool called FLUCCS. An empirical study based on 210
                 real world Java faults suggests that the simple
                 inclusion of ternary conditional operator can help
                 fault localization by placing up to 11percent more
                 faults at the top compared to our baseline, FLUCCS,
                 which in itself can already rank 50percent more faults
                 at the top compared to the state-of-the-art SBFL
                 formulae.",
  notes =        "Also known as \cite{Kang:2017:EEC:3071178.3071263}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Dahyun Kang Jeongju Sohn Shin Yoo

Citations