Evolutionary Fuzzing for Genetic Improvement: Toward Adaptive Software Defense

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Jason Landsborough and Stephen Harding and 
                 Bryan Beabout",
  title =        "Evolutionary Fuzzing for Genetic Improvement: Toward
                 Adaptive Software Defense",
  booktitle =    "GI-2018, ICSE workshops proceedings",
  year =         "2018",
  editor =       "Justyna Petke and Kathryn Stolee and 
                 William B. Langdon and Westley Weimer",
  pages =        "45--46",
  address =      "Gothenburg, Sweden",
  month =        "2 " # jun,
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, genetic
  isbn13 =       "978-1-4503-5753-1",
  URL =          "http://geneticimprovementofsoftware.com/wp-content/uploads/2018/04/GI.pdf",
  DOI =          "doi:10.1145/3194810.3194819",
  size =         "2 pages",
  abstract =     "As fuzz testing strategies have become more and more
                 sophisticated, we see a natural application of fuzz
                 testing to Genetic Improvement techniques. In
                 particular, the ability to generate high quality and
                 high coverage tests with advanced fuzzers can greatly
                 enhance the effectiveness of Genetic Improvement
                 algorithms, especially when the algorithm is applied to
                 bug fixing or other similar kinds of software
                 improvement to improve qualities such as security.",
  notes =        "Slides:

                 GI-2018 http://geneticimprovementofsoftware.com part of

Genetic Programming entries for Jason Landsborough Stephen Harding Bryan Beabout