Using Genetic Programming to Improve Software Effort Estimation Based on General Data Sets

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Martin Lefley and Martin J. Shepperd",
  title =        "Using Genetic Programming to Improve Software Effort
                 Estimation Based on General Data Sets",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2003",
  editor =       "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and 
                 D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and 
                 R. Standish and G. Kendall and S. Wilson and 
                 M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and 
                 A. C. Schultz and K. Dowsland and N. Jonoska and 
                 J. Miller",
  year =         "2003",
  pages =        "2477--2487",
  address =      "Chicago",
  publisher_address = "Berlin",
  month =        "12-16 " # jul,
  volume =       "2724",
  series =       "LNCS",
  ISBN =         "3-540-40603-4",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Search Based
                 Software Engineering",
  DOI =          "doi:10.1007/3-540-45110-2_151",
  abstract =     "various techniques including genetic programming, with
                 public data sets, to attempt to model and hence
                 estimate software project effort. The main research
                 question is whether genetic programs can offer `better'
                 solution search using public domain metrics rather than
                 company specific ones. Unlike most previous research, a
                 realistic approach is taken, whereby predictions are
                 made on the basis of the data available at a given
                 date. Experiments are reported, designed to assess the
                 accuracy of estimates made using data within and beyond
                 a specific company. This research also offers insights
                 into genetic programming's performance, relative to
                 alternative methods, as a problem solver in this
                 domain. The results do not find a clear winner but, for
                 this data, GP performs consistently well, but is harder
                 to configure and produces more complex models. The
                 evidence here agrees with other researchers that
                 companies would do well to base estimates on in house
                 data rather than incorporating public data sets. The
                 complexity of the GP must be weighed against the small
                 increases in accuracy to decide whether to use it as
                 part of any effort prediction estimation.",
  notes =        "GECCO-2003. A joint meeting of the twelfth
                 International Conference on Genetic Algorithms
                 (ICGA-2003) and the eighth Annual Genetic Programming
                 Conference (GP-2003)",

Genetic Programming entries for Martin Lefley Martin J Shepperd