Prediction of fault count data using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Wasif Afzal and Richard Torkar and Robert Feldt",
  title =        "Prediction of fault count data using genetic
  booktitle =    "Proceedings of the 12th IEEE International Multitopic
                 Conference (INMIC'08)",
  year =         "2008",
  pages =        "349--356",
  address =      "Karachi, Pakistan",
  month =        "23-24 " # dec,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, SBSE, fault
                 count data, prediction",
  isbn13 =       "978-1-4244-2823-6",
  URL =          "",
  DOI =          "doi:10.1109/INMIC.2008.4777762",
  abstract =     "Software reliability growth modeling helps in deciding
                 project release time and managing project resources. A
                 large number of such models have been presented in the
                 past. Due to the existence of many models, the models'
                 inherent complexity, and their accompanying
                 assumptions; the selection of suitable models becomes a
                 challenging task. This paper presents empirical results
                 of using genetic programming (GP) for modeling software
                 reliability growth based on weekly fault count data of
                 three different industrial projects. The goodness of
                 fit (adaptability) and predictive accuracy of the
                 evolved model is measured using five different measures
                 in an attempt to present a fair evaluation. The results
                 show that the GP evolved model has statistically
                 significant goodness of fit and predictive accuracy.",
  notes =        "Also known as \cite{4777762}


Genetic Programming entries for Wasif Afzal Richard Torkar Robert Feldt