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A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

by Prashant Johri, Md. Nasar, Udayan Chanda
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 68 - Number 5
Year of Publication: 2013
Authors: Prashant Johri, Md. Nasar, Udayan Chanda
10.5120/11575-6891

Prashant Johri, Md. Nasar, Udayan Chanda . A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort. International Journal of Computer Applications. 68, 5 ( April 2013), 21-25. DOI=10.5120/11575-6891

@article{ 10.5120/11575-6891,
author = { Prashant Johri, Md. Nasar, Udayan Chanda },
title = { A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 68 },
number = { 5 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 21-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume68/number5/11575-6891/ },
doi = { 10.5120/11575-6891 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:27:00.276941+05:30
%A Prashant Johri
%A Md. Nasar
%A Udayan Chanda
%T A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort
%J International Journal of Computer Applications
%@ 0975-8887
%V 68
%N 5
%P 21-25
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Allocation of limited testing efforts to a software development project is a complex task for software managers. The challenges become difficult when the nature of the development process is considered in the dynamic environment. Numerous software reliability growth models have been proposed in last one decade to minimize the whole testing effort expenditures, but generally under static assumption. The main purpose of this article is to distribute total testing resource optimally under dynamic condition. An elaborate optimization policy is proposed using genetic algorithm and numerical example is also demonstrated. Genetic Algorithms (GAs) works with a set of individuals, representing probable solutions of the task. The selection theory is applied by using a criterion, giving an evaluation for the individual with respect to the desired solution. This article also studies the optimal resource allocation problems for different conditions by investigative the activities of the model parameters.

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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm testing effort allocation Software reliability SRGM

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