Can genetic programming improve software effort estimation? A comparative evaluation

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

@Article{Burgess:2001:IST,
  author =       "Colin J. Burgess and Martin Lefley",
  title =        "Can genetic programming improve software effort
                 estimation? A comparative evaluation",
  year =         "2001",
  journal =      "Information and Software Technology",
  volume =       "43",
  number =       "14",
  pages =        "863--873",
  month =        "15 " # dec,
  keywords =     "genetic algorithms, genetic programming, Case-based
                 reasoning, Machine learning, Neural networks, Software
                 effort estimation",
  URL =          "http://www.sciencedirect.com/science/article/B6V0B-44D4196-7/1/20f45986fc0a4827ad09169178379d73",
  DOI =          "doi:10.1016/S0950-5849(01)00192-6",
  abstract-url = "http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000586",
  size =         "11 pages",
  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 (ML) methods. This paper attempts to evaluate
                 critically the potential of genetic programming (GP) in
                 software effort estimation when compared with
                 previously published approaches, in terms of accuracy
                 and ease of use. 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. The input variables are restricted to those
                 available from the specification stage and significant
                 effort is put into the GP and all of the other solution
                 strategies to offer a realistic and fair comparison.
                 There is evidence that GP can offer significant
                 improvements in accuracy but this depends on the
                 measure and interpretation of accuracy used. GP has the
                 potential to be a valid additional tool for software
                 effort estimation but set up and running effort is high
                 and interpretation difficult, as it is for any complex
                 meta-heuristic technique.",
}

Genetic Programming entries for Colin J Burgess Martin Lefley

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