In this study, a genetic programming technique was used with the goal of estimating the effort required in the development of individual projects. Results obtained were compared with those generated by a statistical regression and by a neural network that have already been used to estimate the development effort of individual software projects. A sample of 132 projects developed by 40 programmers was used for generating the three models and another sample of 77 projects developed by 24 programmers was used for validating the three models. Results in the accuracy of the model obtained from genetic programming suggest that it could be used to estimate software development effort of individual projects.
%0 Journal Article
%1 IJACSA.2013.041115
%A Arturo Chavoya Cuauhtemoc Lopez-Martin, M E Meda-Campa&\#241;a
%D 2013
%J International Journal of Advanced Computer Science and Applications(IJACSA)
%K effort estimation; feedforward genetic network; neural programming; regression software statistical
%N 11
%T Software Development Effort Estimation by Means of Genetic Programming
%U http://ijacsa.thesai.org/
%V 4
%X In this study, a genetic programming technique was used with the goal of estimating the effort required in the development of individual projects. Results obtained were compared with those generated by a statistical regression and by a neural network that have already been used to estimate the development effort of individual software projects. A sample of 132 projects developed by 40 programmers was used for generating the three models and another sample of 77 projects developed by 24 programmers was used for validating the three models. Results in the accuracy of the model obtained from genetic programming suggest that it could be used to estimate software development effort of individual projects.
@article{IJACSA.2013.041115,
abstract = {In this study, a genetic programming technique was used with the goal of estimating the effort required in the development of individual projects. Results obtained were compared with those generated by a statistical regression and by a neural network that have already been used to estimate the development effort of individual software projects. A sample of 132 projects developed by 40 programmers was used for generating the three models and another sample of 77 projects developed by 24 programmers was used for validating the three models. Results in the accuracy of the model obtained from genetic programming suggest that it could be used to estimate software development effort of individual projects.},
added-at = {2014-02-21T08:00:08.000+0100},
author = {{Arturo Chavoya Cuauhtemoc Lopez-Martin}, M E Meda-Campa\&\#241;a},
biburl = {https://www.bibsonomy.org/bibtex/2a8a0c43accd7e59d96e83da1a8fdb3f7/thesaiorg},
interhash = {5439ef745ce63ca07c520d6d4318fd8d},
intrahash = {a8a0c43accd7e59d96e83da1a8fdb3f7},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA)},
keywords = {effort estimation; feedforward genetic network; neural programming; regression software statistical},
number = 11,
timestamp = {2014-02-21T08:00:08.000+0100},
title = {{Software Development Effort Estimation by Means of Genetic Programming}},
url = {http://ijacsa.thesai.org/},
volume = 4,
year = 2013
}