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Employing Gene Expression Programming in Estimating Software Effort

by Najla Akram Al-Saati, Taghreed Riyadh Al-Reffaee
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 8
Year of Publication: 2018
Authors: Najla Akram Al-Saati, Taghreed Riyadh Al-Reffaee
10.5120/ijca2018917619

Najla Akram Al-Saati, Taghreed Riyadh Al-Reffaee . Employing Gene Expression Programming in Estimating Software Effort. International Journal of Computer Applications. 182, 8 ( Aug 2018), 1-8. DOI=10.5120/ijca2018917619

@article{ 10.5120/ijca2018917619,
author = { Najla Akram Al-Saati, Taghreed Riyadh Al-Reffaee },
title = { Employing Gene Expression Programming in Estimating Software Effort },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2018 },
volume = { 182 },
number = { 8 },
month = { Aug },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number8/29837-2018917619/ },
doi = { 10.5120/ijca2018917619 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:10:44.658175+05:30
%A Najla Akram Al-Saati
%A Taghreed Riyadh Al-Reffaee
%T Employing Gene Expression Programming in Estimating Software Effort
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 8
%P 1-8
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The problem of estimating the effort for software packages is one of the most significant challenges encountering software designers. The precision in estimating the effort or cost can have a huge impact on software development. Various methods have been investigated in order to discover good enough solutions to this problem; lately evolutionary intelligent techniques are explored like Genetic Algorithms, Genetic Programming, Neural Networks, and Swarm Intelligence. In this work, Gene Expression Programming (GEP) is investigated to show its efficiency in acquiring equations that best estimates software effort. Datasets employed are taken from previous projects. The comparisons of learning and testing results are carried out with COCOMO, Analogy, GP and four types of Neural Networks, all show that GEP outperforms all these methods in discovering effective functions for the estimation with robustness and efficiency.

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Index Terms

Computer Science
Information Sciences

Keywords

Effort Estimation Software Engineering Artificial Intelligence Gene Expression Programming.

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