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Parameter Optimization for Software Metric using Particle Swarm Optimization

Published on July 2016 by Anurag Kumar, Akshay Saxena, Hemant Rai
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National Conference on Next Generation Technologies for e-Business, e-Education and e-Society
Foundation of Computer Science USA
NGTBES2016 - Number 1
July 2016
Authors: Anurag Kumar, Akshay Saxena, Hemant Rai
2616f1c5-a189-413f-99c6-65803117cae6

Anurag Kumar, Akshay Saxena, Hemant Rai . Parameter Optimization for Software Metric using Particle Swarm Optimization. National Conference on Next Generation Technologies for e-Business, e-Education and e-Society. NGTBES2016, 1 (July 2016), 22-25.

@article{
author = { Anurag Kumar, Akshay Saxena, Hemant Rai },
title = { Parameter Optimization for Software Metric using Particle Swarm Optimization },
journal = { National Conference on Next Generation Technologies for e-Business, e-Education and e-Society },
issue_date = { July 2016 },
volume = { NGTBES2016 },
number = { 1 },
month = { July },
year = { 2016 },
issn = 0975-8887,
pages = { 22-25 },
numpages = 4,
url = { /proceedings/ngtbes2016/number1/25544-3509/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Next Generation Technologies for e-Business, e-Education and e-Society
%A Anurag Kumar
%A Akshay Saxena
%A Hemant Rai
%T Parameter Optimization for Software Metric using Particle Swarm Optimization
%J National Conference on Next Generation Technologies for e-Business, e-Education and e-Society
%@ 0975-8887
%V NGTBES2016
%N 1
%P 22-25
%D 2016
%I International Journal of Computer Applications
Abstract

Software Metrics have an important role in Software Development. Cost, Productivity and Quality are specific area of measurement in software metrics. Parameter optimization is great challenge in software metrics. Scientists have used various techniques to optimize the parameter like as Artificial Intelligence, Neural Network and Genetic Algorithm etc. In this thesis, Particle Swarm Optimization (PSO) is proposed as optimization technique. PSO algorithm is a multi-agent parallel search technique which maintains a swarm of particles and each particle represents a potential solution in the swarm. Therefore this austere method is used to work on the parameter optimization in software metrics. An approach of two model structure of PSO has been used for optimizing the parameter. Standard NASA-18 data set is used to evaluate the proposed approach. PSO based models show better result as compared to regression method.

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Index Terms

Computer Science
Information Sciences

Keywords

Particle Swarm Optimization(pso) Regression Mean Magnitude Relative Error(mmre) Nasa 18 Data Set.