Software Project Effort Estimation Using Genetic
Programming
Y. Shan, R. McKay, C. Lokan, and D. Essam. Proceedings of International Conference on
Communications Circuits and Systems, (2002)
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.
%0 Conference Paper
%1 shan:2002:ICCCAS
%A Shan, Y.
%A McKay, R. I.
%A Lokan, C. J.
%A Essam, D. L.
%B Proceedings of International Conference on
Communications Circuits and Systems
%D 2002
%K algorithms, cost engineering, estimation genetic grammar-guided programming, software
%T Software Project Effort Estimation Using Genetic
Programming
%U http://citeseer.ist.psu.edu/545689.html
%X 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.
@inproceedings{shan:2002:ICCCAS,
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.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Shan, Y. and McKay, R. I. and Lokan, C. J. and Essam, D. L.},
biburl = {https://www.bibsonomy.org/bibtex/23909d1d151fc219b03dca7169fc6f980/brazovayeye},
booktitle = {Proceedings of International Conference on
Communications Circuits and Systems},
broken = {http://www.cs.adfa.edu.au/~shanyin/publications/soft.ps.Z},
interhash = {4b2b5c08ce7a8ab38705f02d0753c938},
intrahash = {3909d1d151fc219b03dca7169fc6f980},
keywords = {algorithms, cost engineering, estimation genetic grammar-guided programming, software},
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/.},
timestamp = {2008-06-19T17:51:32.000+0200},
title = {Software Project Effort Estimation Using Genetic
Programming},
url = {http://citeseer.ist.psu.edu/545689.html},
year = 2002
}