Can Genetic Programming improve Software Effort
Estimation? A Comparative Evaluation
C. Burgess, and M. Lefley. Machine Learning Applications In Software Engineering:
Series on Software Engineering and Knowledge
Engineering, 16, World Scientific Publishing Co., (May 2005)
Abstract
Accurate software effort estimation is an important
part of the software process. Originally, estimation
was performed using only human expertise, but more
recently attention has turned to a variety of machine
learning methods. This paper attempts to critically
evaluate the potential of genetic programming (GP) in
software effort estimation when compared with
previously published approaches. The comparison is
based on the well-known Desharnais data set of 81
software projects derived from a Canadian software
house in the late 1980s. It shows that GP can offer
some significant improvements in accuracy and has the
potential to be a valid additional tool for software
effort estimation.
%0 Book Section
%1 2000240
%A Burgess, C. J.
%A Lefley, M.
%B Machine Learning Applications In Software Engineering:
Series on Software Engineering and Knowledge
Engineering
%D 2005
%E Zhang, Du
%E Tsai, Jeffrey J. P.
%I World Scientific Publishing Co.
%K Artificial Intelligence, Learning Machine algorithms, genetic programming,
%P 95--105
%T Can Genetic Programming improve Software Effort
Estimation? A Comparative Evaluation
%V 16
%X Accurate software effort estimation is an important
part of the software process. Originally, estimation
was performed using only human expertise, but more
recently attention has turned to a variety of machine
learning methods. This paper attempts to critically
evaluate the potential of genetic programming (GP) in
software effort estimation when compared with
previously published approaches. The comparison is
based on the well-known Desharnais data set of 81
software projects derived from a Canadian software
house in the late 1980s. It shows that GP can offer
some significant improvements in accuracy and has the
potential to be a valid additional tool for software
effort estimation.
%@ 981-256-094-7
@incollection{2000240,
abstract = {Accurate software effort estimation is an important
part of the software process. Originally, estimation
was performed using only human expertise, but more
recently attention has turned to a variety of machine
learning methods. This paper attempts to critically
evaluate the potential of genetic programming (GP) in
software effort estimation when compared with
previously published approaches. The comparison is
based on the well-known Desharnais data set of 81
software projects derived from a Canadian software
house in the late 1980s. It shows that GP can offer
some significant improvements in accuracy and has the
potential to be a valid additional tool for software
effort estimation.},
abstract-url = {http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=2000240},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Burgess, C. J. and Lefley, M.},
biburl = {https://www.bibsonomy.org/bibtex/2344982548feadba8a6fb5f8d34e75d98/brazovayeye},
booktitle = {Machine Learning Applications In Software Engineering:
Series on Software Engineering and Knowledge
Engineering},
editor = {Zhang, Du and Tsai, Jeffrey J. P.},
interhash = {607b184f0d70e6340bc5e2891062ebc0},
intrahash = {344982548feadba8a6fb5f8d34e75d98},
isbn = {981-256-094-7},
keywords = {Artificial Intelligence, Learning Machine algorithms, genetic programming,},
month = May,
notes = {This paper is not on-line. Contact the author},
pages = {95--105},
publisher = {World Scientific Publishing Co.},
pubtype = {7},
timestamp = {2008-06-19T17:37:08.000+0200},
title = {Can Genetic Programming improve Software Effort
Estimation? {A} Comparative Evaluation},
volume = 16,
year = 2005
}