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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.11, No.1, Jan. 2019

Effort Estimation of Back-end Part of Software using Chaotically Modified Genetic Algorithm

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Author(s)

Saurabh Bilgaiyan, Dhiraj Kumar Goswami, Samaresh Mishra, Madhabananda Das

Index Terms

SDEE;genetic algorithm (GA);evolutionary learning (EL);dilation-erosion perceptron (DEP);mathematical morphology (MM)

Abstract

The focus of Software Development Effort Estimation (SDEE) is to precisely predict the estimation of effort and time required for successfully developing a software project. From the past few years, data-intensive applications with a huge back-end part are contributing to the overall effort of projects. Therefore, it is becoming more important to add the back-end part to the SDEE process. This paper proposes an Evolutionary Learning (EL) based hybrid artificial neuron termed as dilation-erosion perceptron (DEP) framework from the mathematical morphology (MM) having its foundation in complete lattice theory (CLT) for solving the SDEE problem. In this work, we used the DEP (CMGA) model utilizing a chaotically modified genetic algorithm (CMGA) for the construction of DEP parameters. The proposed method uses the ER diagram artifacts such as aggregation, specialization, generalization, semantic integrity constraints, etc. for calculating the SDEE of back-end part of the business software. Furthermore, the proposed method was tested over two different datasets, one is existing and the other one is a self-developed dataset. The performance of the given method is then evaluated by three popular performance metrics, exhibiting better performance of the DEP (CMGA) model for solving the SDEE problems.

Cite This Paper

Saurabh Bilgaiyan, Dhiraj Kumar Goswami, Samaresh Mishra, Madhabananda Das, "Effort Estimation of Back-end Part of Software using Chaotically Modified Genetic Algorithm", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.1, pp.32-42, 2019. DOI: 10.5815/ijisa.2019.01.04

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