Searching for invariants using genetic programming and mutation testing

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Sam Ratcliff and David R. White and John A. Clark",
  title =        "Searching for invariants using genetic programming and
                 mutation testing",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0557-0",
  pages =        "1907--1914",
  note =         "Best paper",
  keywords =     "genetic algorithms, genetic programming, Search-based
                 software engineering",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001832",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Invariants are concise and useful descriptions of a
                 program's behaviour. As most programs are not annotated
                 with invariants, previous research has attempted to
                 automatically generate them from source code. In this
                 paper, we propose a new approach to invariant
                 generation using search. We reuse the trace generation
                 front-end of existing tool Daikon and integrate it with
                 genetic programming and a mutation testing tool. We
                 demonstrate that our system can find the same
                 invariants through search that Daikon produces via
                 template instantiation, and we also find useful
                 invariants that Daikon does not. We then present a
                 method of ranking invariants such that we can identify
                 those that are most interesting, through a novel
                 application of program mutation.",
  notes =        "Also known as \cite{2001832} GECCO-2011 A joint
                 meeting of the twentieth international conference on
                 genetic algorithms (ICGA-2011) and the sixteenth annual
                 genetic programming conference (GP-2011)",

Genetic Programming entries for Sam Ratcliff David Robert White John A Clark