Genetic programming model for software quality classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4215

  author =       "Yi Liu and Taghi M. Khoshgoftaar",
  title =        "Genetic programming model for software quality
  booktitle =    "Sixth IEEE International Symposium on High Assurance
                 Systems Engineering, HASE'01",
  year =         "2001",
  pages =        "127--136",
  address =      "Boco Raton, FL, USA",
  month =        oct # " 22-24",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming,
                 classification, evolutionary computation, software
                 qualitygenetic programming, quality classification,
                 software engineering, software metrics, software
                 quality, SBSE",
  ISBN =         "0-7695-1275-5",
  DOI =          "doi:10.1109/HASE.2001.966814",
  size =         "10 pages",
  abstract =     "We apply genetic programming techniques to build a
                 software quality classification model based on the
                 metrics of software modules. The model we built
                 attempts to distinguish the fault-prone modules from
                 non-fault-prone modules using genetic programming (GP).
                 These GP experiments were conducted with a random
                 subset selection for GP in order to avoid overfitting.
                 We then use the whole fit data set as the validation
                 data set to select the best model. We demonstrate
                 through two case studies that the GP technique can
                 achieve good results. Also, we compared GP modeling
                 with logistic regression modeling to verify the
                 usefulness of GP",
  notes =        "Also known as \cite{966814} INSPEC Accession

                 p126 {"}VLWA{"} C++ {"}over 27.5 million lines of
                 code{"}. Logistic Regression LRM",

Genetic Programming entries for Yi Liu Taghi M Khoshgoftaar