Improved crossover operators for genetic programming for program repair

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Oliveira:2016:SSBSE,
  author =       "Vinicius Paulo L. Oliveira and Eduardo F. D. Souza and 
                 Claire {Le Goues} and Celso G. Camilo-Junior",
  title =        "Improved crossover operators for genetic programming
                 for program repair",
  booktitle =    "Proceedings of the 8th International Symposium on
                 Search Based Software Engineering, SSBSE 2016",
  year =         "2016",
  editor =       "Federica Sarro and Kalyanmoy Deb",
  volume =       "9962",
  series =       "LNCS",
  pages =        "112--127",
  address =      "Raleigh, North Carolina, USA",
  month =        "8-10 " # oct,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement, SBSE, Automatic software repair, Automated
                 program repair, Evolutionary computation, Crossover
                 operator",
  isbn13 =       "978-3-319-47106-8",
  DOI =          "doi:10.1007/978-3-319-47106-8_8",
  size =         "16 pages",
  abstract =     "GenProg is a stochastic method based on genetic
                 programming that presents promising results in
                 automatic software repair via patch evolution.
                 GenProg's crossover operates on a patch representation
                 composed of high-granularity edits that indivisibly
                 comprise an edit operation, a faulty location, and a
                 fix statement used in replacement or insertions.
                 Recombination of such high-level minimal units limits
                 the technique's ability to effectively traverse and
                 recombine the repair search spaces. In this work, we
                 propose a reformulation of program repair operators
                 such that they explicitly traverse three subspaces that
                 underlie the search problem: Operator, Fault Space and
                 Fix Space. We leverage this reformulation in the form
                 of new crossover operators that faithfully respect this
                 subspace division, improving search performance. Our
                 experiments on 43 programs validate our insight, and
                 show that the Unif1Space without memorization performed
                 best, improving the fix rate by 34percent.",
  notes =        "Fig 2 representation where parts of patch are placed
                 together versus whole of patch being adjacent. gcd,
                 zune, checksum, digits, grade, median, smallest,
                 syllables.

                 SSBSE 2016 co-located with ICSME-2016

                 ",
}

Genetic Programming entries for Vinicius Paulo L Oliveira Eduardo F de Souza Claire Le Goues Celso G Camilo Jr

Citations