Prediction of faults-slip-through in large software projects: an empirical evaluation

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

@Article{Afzal:2013:SQJ,
  author =       "Wasif Afzal and Richard Torkar and Robert Feldt and 
                 Tony Gorschek",
  title =        "Prediction of faults-slip-through in large software
                 projects: an empirical evaluation",
  journal =      "Software Quality Journal",
  year =         "2014",
  volume =       "22",
  number =       "1",
  pages =        "51--86",
  month =        mar,
  publisher =    "Springer US",
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 Prediction, Empirical, Faults-slip-through,
                 Search-based",
  ISSN =         "0963-9314",
  DOI =          "doi:10.1007/s11219-013-9205-3",
  language =     "English",
  oai =          "oai:bth.se:forskinfo3D40224F7CBF862DC1257B7800251E66",
  URL =          "http://www.bth.se/fou/forskinfo.nsf/all/3d40224f7cbf862dc1257b7800251e66?OpenDocument",
  size =         "36 pages",
  abstract =     "A large percentage of the cost of rework can be
                 avoided by finding more faults earlier in a software
                 test process. Therefore, determination of which
                 software test phases to focus improvement work on has
                 considerable industrial interest. We evaluate a number
                 of prediction techniques for predicting the number of
                 faults slipping through to unit, function, integration,
                 and system test phases of a large industrial project.
                 The objective is to quantify improvement potential in
                 different test phases by striving toward finding the
                 faults in the right phase. The results show that a
                 range of techniques are found to be useful in
                 predicting the number of faults slipping through to the
                 four test phases; however, the group of search-based
                 techniques (genetic programming, gene expression
                 programming, artificial immune recognition system, and
                 particle swarm optimisation (PSO) based artificial
                 neural network) consistently give better predictions,
                 having a representation at all of the test phases.
                 Human predictions are consistently better at two of the
                 four test phases. We conclude that the human
                 predictions regarding the number of faults slipping
                 through to various test phases can be well supported by
                 the use of search-based techniques. A combination of
                 human and an automated search mechanism (such as any of
                 the search-based techniques) has the potential to
                 provide improved prediction results.",
}

Genetic Programming entries for Wasif Afzal Richard Torkar Robert Feldt Tony Gorschek

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