Learning Fault Localisation for Both Humans and Machines using Multi-Objective GP

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

@InProceedings{Choi:2018:SSBSE,
  author =       "Kabdo Choi and Jeongju Sohn and Shin Yoo",
  title =        "Learning Fault Localisation for Both Humans and
                 Machines using Multi-Objective GP",
  booktitle =    "SSBSE 2018",
  year =         "2018",
  editor =       "Thelma Elita Colanzi and Phil McMinn",
  volume =       "11036",
  series =       "LNCS",
  pages =        "349--355",
  address =      "Montpellier, France",
  month =        "8-9 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, SBSE, MOGP,
                 Fault localisation, FLUCCS, Multi-objective
                 evolutionary algorithm, NSGA-II",
  isbn13 =       "978-3-319-99241-9",
  DOI =          "doi:10.1007/978-3-319-99241-9_20",
  abstract =     "Genetic Programming has been successfully applied to
                 fault localisation to learn ranking models that place
                 the faulty program element as near the top as possible.
                 However, it is also known that, when localisation
                 results are used by Automatic Program Repair (APR)
                 techniques, higher rankings of faulty program elements
                 do not necessarily result in better repair
                 effectiveness. Since APR techniques tend to use
                 localisation scores as weights for program mutation,
                 lower scores for non-faulty program elements are as
                 important as high scores for faulty program elements.
                 We formulate a multi-objective version of GP based
                 fault localisation to learn ranking models that not
                 only aim to place the faulty program element higher in
                 the ranking, but also aim to assign as low scores as
                 possible to non-faulty program elements. The results
                 show minor improvements in the suspiciousness score
                 distribution. However, surprisingly, the
                 multi-objective formulation also results in more
                 accurate fault localisation ranking-wise, placing 155
                 out of 386 faulty methods at the top, compared to 135
                 placed at the top by the single objective
                 formulation.",
}

Genetic Programming entries for Kabdo Choi Jeongju Sohn Shin Yoo

Citations