Efficient multi-objective higher order mutation testing with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "William B. Langdon and Mark Harman and Yue Jia",
  title =        "Efficient multi-objective higher order mutation
                 testing with genetic programming",
  journal =      "Journal of Systems and Software",
  year =         "2010",
  volume =       "83",
  number =       "12",
  pages =        "2416--2430",
  month =        dec,
  notes =        "TAIC PART 2009 - Testing: Academic \& Industrial
                 Conference - Practice And Research Techniques",
  keywords =     "genetic algorithms, genetic programming, Pareto
                 optimality, mutation testing, higher order mutation,
                 SBSE, Monte Carlo, NSGA-II, strongly typed GP, grammar
                 based GP, non-determinism, triangle, schedule, tcas,
  ISSN =         "0164-1212",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_jss.pdf",
  URL =          "http://www.sciencedirect.com/science/article/B6V0N-50J9GRW-3/2/b1769fb8aaf4ea90164109756b3e2dc6",
  DOI =          "doi:10.1016/j.jss.2010.07.027",
  size =         "21 pages",
  abstract =     "It is said ninety percent of faults that survive
                 manufacturer's testing procedures are complex. That is,
                 the corresponding bug fix contains multiple changes.
                 Higher order mutation testing is used to study defect
                 interactions and their impact on software testing for
                 fault finding. We adopt a multi-objective Pareto
                 optimal approach using Monte Carlo sampling, genetic
                 algorithms and genetic programming to search for higher
                 order mutants which are both hard-to-kill and
                 realistic. The space of complex faults (higher order
                 mutants) is much larger than that of traditional first
                 order mutations which correspond to simple faults,
                 nevertheless search based approaches make this
                 scalable. The problems of non-determinism and
                 efficiency are overcome. Easy to detect faults may
                 become harder to detect when they interact and
                 impossible to detect single faults may be brought to
                 light when code contains two such faults. We use strong
                 typing and BNF grammars in search based mutation
                 testing to find examples of both in ancient heavily
                 optimised every day C code.",
  notes =        "Extended version of \cite{langdon:2009:TAICPART}

Genetic Programming entries for William B Langdon Mark Harman Yue Jia