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Software Assessment Parameter Optimization using Genetic Algorithm

by Neha Sharma, Amit Sinhal, Bhupendra Verma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 7
Year of Publication: 2013
Authors: Neha Sharma, Amit Sinhal, Bhupendra Verma
10.5120/12504-8393

Neha Sharma, Amit Sinhal, Bhupendra Verma . Software Assessment Parameter Optimization using Genetic Algorithm. International Journal of Computer Applications. 72, 7 ( June 2013), 8-13. DOI=10.5120/12504-8393

@article{ 10.5120/12504-8393,
author = { Neha Sharma, Amit Sinhal, Bhupendra Verma },
title = { Software Assessment Parameter Optimization using Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 7 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 8-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number7/12504-8393/ },
doi = { 10.5120/12504-8393 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:37:16.129185+05:30
%A Neha Sharma
%A Amit Sinhal
%A Bhupendra Verma
%T Software Assessment Parameter Optimization using Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 7
%P 8-13
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software assessment of a project is a key aspect for the prediction of the cost, duration and the expertise required for the project. An efficient optimization algorithm is urgently needed. In this paper, we analyze the genetic algorithm (GA) technique for the development of a software assessment model for the NASA software project dataset. The simulation is performed using MATLAB environment and the results are tested on the basis of measures such as MMRE, MdMRE, MMER, Prediction Accuracy (25%) and the estimation time. The results of the developed Genetic Algorithm (GA) based model was also compared to known models in the literature. The assessment provided by the developed GA model was good compared to other models.

References
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Index Terms

Computer Science
Information Sciences

Keywords

COCOMO model Genetic algorithm Genetic programming NASA software

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