Multi Objective Higher Order Mutation Testing with GP

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "W. B. Langdon and Mark Harman and Yue Jia",
  title =        "Multi Objective Higher Order Mutation Testing with
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "1945",
  address =      "Montreal",
  month =        "8-12 " # jul,
  organisation = "SIGEVO",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, Poster,
                 strongly typed genetic programming, grammar, Pareto
                 optimality, mutation testing, higher order mutation,
                 Indirect encoding, Software engineering, triangle,
                 schedule, tcas",
  isbn13 =       "978-1-60558-325-9",
  URL =          "",
  DOI =          "doi:10.1145/1569901.1570251",
  size =         "1 page",
  abstract =     "Mutation testing is a powerful software engineering
                 technique for fault finding. It works by injecting
                 known faults (mutations) into software and seeing if
                 the test suite finds them. It remains very expensive
                 and the few valuable traditional mutants that resemble
                 real faults are mixed in with many others that denote
                 unrealistic faults. The expense and lack of realism
                 inhibit industrial uptake of mutation testing. Genetic
                 programming searches the space of complex faults to
                 find realistic higher order mutants. Despite the much
                 larger search space, we have found mutants composed of
                 multiple changes to the C source code that challenge
                 the tester and which cannot be represented in the first
                 order space.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 t14pp387, replaced by \cite{langdon:2009:TAICPART} (10

                 ACM Order Number 910092. Also known as

Genetic Programming entries for William B Langdon Mark Harman Yue Jia