Can Genetic Programming improve Software Effort Estimation? A Comparative Evaluation

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "C. J. Burgess and M. Lefley",
  title =        "Can Genetic Programming improve Software Effort
                 Estimation? {A} Comparative Evaluation",
  booktitle =    "Machine Learning Applications In Software Engineering:
                 Series on Software Engineering and Knowledge
  editor =       "Du Zhang and Jeffrey J. P. Tsai",
  volume =       "16",
  ISBN =         "981-256-094-7",
  publisher =    "World Scientific Publishing Co.",
  pages =        "95--105",
  month =        may,
  year =         "2005",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 Intelligence, Machine Learning, SBSE",
  pubtype =      "7",
  broken =       "",
  abstract =     "Accurate software effort estimation is an important
                 part of the software process. Originally, estimation
                 was performed using only human expertise, but more
                 recently attention has turned to a variety of machine
                 learning methods. This paper attempts to critically
                 evaluate the potential of genetic programming (GP) in
                 software effort estimation when compared with
                 previously published approaches. The comparison is
                 based on the well-known Desharnais data set of 81
                 software projects derived from a Canadian software
                 house in the late 1980s. It shows that GP can offer
                 some significant improvements in accuracy and has the
                 potential to be a valid additional tool for software
                 effort estimation.",
  notes =        "This paper is not on-line. Contact the author see

Genetic Programming entries for Colin J Burgess Martin Lefley