Multi Objective Mutation Testing with Genetic Programming

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

@InProceedings{langdon:2009:TAICPART,
  author =       "William B. Langdon and Mark Harman and Yue Jia",
  title =        "Multi Objective Mutation Testing with Genetic
                 Programming",
  booktitle =    "TAIC-PART",
  year =         "2009",
  editor =       "Leonardo Bottaci and Gregory Kapfhammer and 
                 Neil Walkinshaw",
  pages =        "21--29",
  address =      "Windsor, UK",
  month =        "4-6 " # sep,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, strongly
                 typed genetic programming, grammar, Pareto optimality,
                 mutation testing, higher order mutation, Indirect
                 encoding, Software engineering, SBSE, triangle,
                 schedule, tcas",
  isbn13 =       "978-0-7695-3820-4",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2009_TAICPART.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2009_TAICPART.ps.gz",
  DOI =          "doi:10.1109/TAICPART.2009.18",
  size =         "10 pages",
  abstract =     "In academic empirical studies, mutation testing has
                 been demonstrated to be a powerful technique for fault
                 finding. However, it remains very expensive and the few
                 valuable traditional mutants that resemble real faults
                 are mixed in with many others that denote unrealistic
                 faults. These twin problems of expense and realism have
                 been a significant barrier to industrial uptake of
                 mutation testing. Genetic programming is used to search
                 the space of complex faults (higher order mutants). The
                 space is much larger than the traditional first order
                 mutation space of simple faults. However, the use of a
                 search based approach makes this scalable, seeking only
                 those mutants that challenge the tester, while the
                 consideration of complex faults addresses the problem
                 of fault realism; it is known that 90percent of real
                 faults are complex (i.e. higher order). We show that we
                 are able to find examples that pose challenges to
                 testing in the higher order space that cannot be
                 represented in the first order space.",
  notes =        "replaces \cite{langdon:2009:gecco2}",
}

Genetic Programming entries for William B Langdon Mark Harman Yue Jia

Citations