On the application of genetic programming for software engineering predictive modeling: A systematic review

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@Article{Afzal201111984,
  author =       "Wasif Afzal and Richard Torkar",
  title =        "On the application of genetic programming for software
                 engineering predictive modeling: A systematic review",
  journal =      "Expert Systems with Applications",
  volume =       "38",
  number =       "9",
  pages =        "11984--11997",
  year =         "2011",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2011.03.041",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-52C8FT6-5/2/668361024e4b2bcf9a4a73195271591c",
  keywords =     "genetic algorithms, genetic programming, Systematic
                 review, Symbolic regression, Modelling",
  abstract =     "The objective of this paper is to investigate the
                 evidence for symbolic regression using genetic
                 programming (GP) being an effective method for
                 prediction and estimation in software engineering, when
                 compared with regression/machine learning models and
                 other comparison groups (including comparisons with
                 different improvements over the standard GP algorithm).
                 We performed a systematic review of literature that
                 compared genetic programming models with comparative
                 techniques based on different independent project
                 variables. A total of 23 primary studies were obtained
                 after searching different information sources in the
                 time span 1995-2008. The results of the review show
                 that symbolic regression using genetic programming has
                 been applied in three domains within software
                 engineering predictive modeling: (i) Software quality
                 classification (eight primary studies). (ii) Software
                 cost/effort/size estimation (seven primary studies).
                 (iii) Software fault prediction/software reliability
                 growth modelling (eight primary studies). While there
                 is evidence in support of using genetic programming for
                 software quality classification, software fault
                 prediction and software reliability growth modelling;
                 the results are inconclusive for software
                 cost/effort/size estimation.",
}

Genetic Programming entries for Wasif Afzal Richard Torkar

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