Evolving Software Effort Estimation Models Using Multigene Symbolic Regression Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Aljahdali:2013:IJARAI,
  author =       "Sultan Aljahdali and Alaa Sheta",
  title =        "Evolving Software Effort Estimation Models Using
                 Multigene Symbolic Regression Genetic Programming",
  journal =      "International Journal of Advanced Research in
                 Artificial Intelligence",
  year =         "2013",
  number =       "12",
  volume =       "2",
  pages =        "52--57",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  publisher =    "The Science and Information (SAI) Organization",
  bibsource =    "OAI-PMH server at thesai.org",
  language =     "eng",
  oai =          "oai:thesai.org:10.14569/IJARAI.2013.021207",
  URL =          "http://thesai.org/Downloads/IJARAI/Volume2No12/Paper_7-Evolving_Software_Effort_Estimation_Models_Using.pdf",
  URL =          "http://dx.doi.org/10.14569/IJARAI.2013.021207",
  size =         "6 pages",
  abstract =     "Software has played an essential role in engineering,
                 economic development, stock market growth and military
                 applications. Mature software industry count on highly
                 predictive software effort estimation models. Correct
                 estimation of software effort lead to correct
                 estimation of budget and development time. It also
                 allows companies to develop appropriate time plan for
                 marketing campaign. Now a day it became a great
                 challenge to get these estimates due to the increasing
                 number of attributes which affect the software
                 development life cycle. Software cost estimation models
                 should be able to provide sufficient confidence on its
                 prediction capabilities. Recently, Computational
                 Intelligence (CI) paradigms were explored to handle the
                 software effort estimation problem with promising
                 results. In this paper we evolve two new models for
                 software effort estimation using Multigene Symbolic
                 Regression Genetic Programming (GP). One model uses the
                 Source Line Of Code (SLOC) as input variable to
                 estimate the Effort (E); while the second model uses
                 the Inputs, Outputs, Files, and User Enquiries to
                 estimate the Function Point (FP). The proposed GP
                 models show better estimation capabilities compared to
                 other reported models in the literature. The validation
                 results are accepted based Albrecht data set.",
}

Genetic Programming entries for Sultan Aljahdali Alaa Sheta

Citations