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Article:SEEPC: A Toolbox for Software Effort Estimation using Soft Computing Techniques

by Hari .CH.V.M.K, Tegjyot Singh Sethi, Jagadeesh.M
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 31 - Number 4
Year of Publication: 2011
Authors: Hari .CH.V.M.K, Tegjyot Singh Sethi, Jagadeesh.M

Hari .CH.V.M.K, Tegjyot Singh Sethi, Jagadeesh.M . Article:SEEPC: A Toolbox for Software Effort Estimation using Soft Computing Techniques. International Journal of Computer Applications. 31, 4 ( October 2011), 12-19. DOI=10.5120/3811-5262

@article{ 10.5120/3811-5262,
author = { Hari .CH.V.M.K, Tegjyot Singh Sethi, Jagadeesh.M },
title = { Article:SEEPC: A Toolbox for Software Effort Estimation using Soft Computing Techniques },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 31 },
number = { 4 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 12-19 },
numpages = {9},
url = { },
doi = { 10.5120/3811-5262 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:17:15.092711+05:30
%A Hari .CH.V.M.K
%A Tegjyot Singh Sethi
%A Jagadeesh.M
%T Article:SEEPC: A Toolbox for Software Effort Estimation using Soft Computing Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 31
%N 4
%P 12-19
%D 2011
%I Foundation of Computer Science (FCS), NY, USA

Software Effort estimation is the process of gauging the amount of effort required to complete the project. With the proliferation of software projects and the heterogeneity in there genre, there is a need for efficient software effort estimation techniques to enable the project managers to perform proper planning of the Software Life Cycle activates. In this article, a new hybrid toolbox based on soft computing techniques for effort estimation is introduced. Particle swarm optimization and cluster analysis has been implemented to perform efficient estimation of effort values with learning ability. The main aim of the toolbox is to provide an efficient, flexible and user friendly way of performing the effort estimation task, by catering to the needs of both the technical and the nontechnical users. The toolbox also implements the COCOMO model to enable a comparative analysis of the proposed model. It was observed that the model when provided with enough training data gave better results when compared with the standard COCOMO values.

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Index Terms

Computer Science
Information Sciences


Constructive Cost Model (COCOMO) K-means algorithm Particle Swarm Optimization (PSO) Software Effort Estimation SEEPC: Software Effort Estimation–PSO–Clustering