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Reseach Article

Development of Simple Effort Estimation Model based on Fuzzy Logic using Bayesian Networks

Published on None 2011 by Abou Bakar Nauman, Romana Aziz
Artificial Intelligence Techniques - Novel Approaches & Practical Applications
Foundation of Computer Science USA
AIT - Number 3
None 2011
Authors: Abou Bakar Nauman, Romana Aziz
e72dbc40-2c43-4221-b28d-14e256a22382

Abou Bakar Nauman, Romana Aziz . Development of Simple Effort Estimation Model based on Fuzzy Logic using Bayesian Networks. Artificial Intelligence Techniques - Novel Approaches & Practical Applications. AIT, 3 (None 2011), 4-7.

@article{
author = { Abou Bakar Nauman, Romana Aziz },
title = { Development of Simple Effort Estimation Model based on Fuzzy Logic using Bayesian Networks },
journal = { Artificial Intelligence Techniques - Novel Approaches & Practical Applications },
issue_date = { None 2011 },
volume = { AIT },
number = { 3 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 4-7 },
numpages = 4,
url = { /specialissues/ait/number3/2836-217/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%A Abou Bakar Nauman
%A Romana Aziz
%T Development of Simple Effort Estimation Model based on Fuzzy Logic using Bayesian Networks
%J Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%@ 0975-8887
%V AIT
%N 3
%P 4-7
%D 2011
%I International Journal of Computer Applications
Abstract

Intelligent software estimation models are need of the time. With increased development of Bayesian networks for software project management, one requires an explicit Bayesian Network (BN) to provide effort estimates based on historical data. This paper proposes a simple BN, based on classification approach. However the classes of ranges of size value, are distributed with help of fuzzification to distribute the probability of crisp value The model is simple and smaller, thus can easily be connected to static as well as dynamic Bayesian Networks.

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Index Terms

Computer Science
Information Sciences

Keywords

Bayesian Networks Fuzzy logic