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Reseach Article

Perceptual Evolution for Software Project Cost Estimation using Ant Colony System

by Nikhat Akhtar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 14
Year of Publication: 2013
Authors: Nikhat Akhtar
10.5120/14185-2385

Nikhat Akhtar . Perceptual Evolution for Software Project Cost Estimation using Ant Colony System. International Journal of Computer Applications. 81, 14 ( November 2013), 23-30. DOI=10.5120/14185-2385

@article{ 10.5120/14185-2385,
author = { Nikhat Akhtar },
title = { Perceptual Evolution for Software Project Cost Estimation using Ant Colony System },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 14 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 23-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number14/14185-2385/ },
doi = { 10.5120/14185-2385 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:56:03.934885+05:30
%A Nikhat Akhtar
%T Perceptual Evolution for Software Project Cost Estimation using Ant Colony System
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 14
%P 23-30
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

My proposed work is inspired by the experiment that uses expert judgment for estimation of the cost on the basis of previous project results. In this paper estimator can use Analogical strategies as well as Algorithmic Strategies as they wish. The proposed method is divided into two phases. First phase computed the probability of each selected factors by ant colony system. Second phase combines the value of these factors to calculate the cost overhead for the project by using Bayesian belief network. Once this overhead is computed productivity is directly calculated which can be converted in effort and cost. Our computation gives the Cost Overhead that depends on various factors. Till date Ant Colony Optimization Algorithm has provided solutions for the problems that have multiple solution and user are interested in best solution. This algorithm provides a proper heuristic for the problem and computes the best possible solution. It gives the solutions in terms of probability, i. e. The most likely occurred solution and the best solution. It was first introduced in Travelling Salesman Problem for finding the minimum cost path. We have mapped our problem in a simple graph by using a questionnaire. That gives the minimum length path, the path that obtains minimum deviation from the nominal project for each factor and theirs encouraging results from proposed technique.

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Index Terms

Computer Science
Information Sciences

Keywords

Cost Estimation Bayesian network Ant Colony Algorithmic-Estimation Strategy Optimization Swarm Intelligence.