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Design and Implementation of Neuro Fuzzy model for Software Development Time Estimation

by Shina Dhingra, Palvinder Singh Mann
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 5
Year of Publication: 2014
Authors: Shina Dhingra, Palvinder Singh Mann
10.5120/14979-3179

Shina Dhingra, Palvinder Singh Mann . Design and Implementation of Neuro Fuzzy model for Software Development Time Estimation. International Journal of Computer Applications. 86, 5 ( January 2014), 7-12. DOI=10.5120/14979-3179

@article{ 10.5120/14979-3179,
author = { Shina Dhingra, Palvinder Singh Mann },
title = { Design and Implementation of Neuro Fuzzy model for Software Development Time Estimation },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 5 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 7-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number5/14979-3179/ },
doi = { 10.5120/14979-3179 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:03:24.195425+05:30
%A Shina Dhingra
%A Palvinder Singh Mann
%T Design and Implementation of Neuro Fuzzy model for Software Development Time Estimation
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 5
%P 7-12
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

To develop a project successfully, it is important for any organization that the project should be completed within budget, on time and the project should have requisite quality. This paper presents an Adaptive Neuro-Fuzzy Approach for Software Development Time Estimation. This proposed technique is aimed at building and evaluating a Neuro - fuzzy model using three (3) membership functions (MFs) for software project development time. The forty one modules were used as a data set. Our proposed approach for Neuro fuzzy using 3 membership functions i. e. Gaussian MF (GMF), Triangular MF (Tri MF) and Trapezoidal MF (Trap MF) is compared with neural network models and the results show that values of various relative error parameters for Neuro-fuzzy is lower than the values of parameters applying neural network.

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Index Terms

Computer Science
Information Sciences

Keywords

Adaptive Neuro Fuzzy Inference System (ANFIS) Neural Network Fuzzy Logic Prediction MRE MMRE BRE Development Time (DT) Membership Function (MF).

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