Software Project Effort Estimation Using Genetic Programming

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

@InProceedings{shan:2002:ICCCAS,
  author =       "Y. Shan and R. I. McKay and C. J. Lokan and 
                 D. L. Essam",
  title =        "Software Project Effort Estimation Using Genetic
                 Programming",
  booktitle =    "IEEE 2002 International Conference on Communications,
                 Circuits and Systems and West Sino Expositions",
  year =         "2002",
  month =        jun # "-1 " # jul,
  volume =       "2",
  pages =        "1108--1112",
  keywords =     "genetic algorithms, genetic programming,
                 grammar-guided genetic programming, software
                 engineering, software cost estimation, SBSE, GGGP
                 background knowledge, GGGP evolutionary computation
                 methods, linear regression, model fitting, software
                 cost prediction, software development cycle management,
                 software engineering modelling, software project cost
                 estimation, software project effort estimation,
                 evolutionary computation, grammars, project management,
                 software development management",
  URL =          "http://www.cs.adfa.edu.au/~shanyin/publications/soft.pdf",
  broken =       "http://www.cs.adfa.edu.au/~shanyin/publications/soft.ps.Z",
  URL =          "http://citeseer.ist.psu.edu/545689.html",
  DOI =          "doi:10.1109/ICCCAS.2002.1178979",
  size =         "5 pages",
  abstract =     "Knowing the estimated cost of a software project early
                 in the development cycle is a valuable asset for
                 management. In this paper, an evolutionary computation
                 method, Grammar Guided Genetic Programming (GGGP), is
                 used to fit models, with the aim of improving the
                 prediction of software development costs. Valuable
                 results are obtained, significantly better than those
                 obtained by simple linear regression. In this research,
                 GGGP, because of its flexibility and the ability of
                 incorporating background knowledge, also shows great
                 potential in being applied in other software
                 engineering modelling problems.",
  notes =        "(ICCCAS'2002), June 29-July 1 2002, Chengdu, Sichuan,
                 PR of China, sponsored by IEEE, NSFC, CIC, CIE, CCPIT,
                 UESTC. For details, visit:
                 http://icccas02.uestc.edu.cn/ (broken 2012).

                 Also known as \cite{1178979}",
}

Genetic Programming entries for Yin Shan R I (Bob) McKay C J Lokan Daryl Essam

Citations