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Journal of Intelligent Systems

Editor-in-Chief: Fleyeh, Hasan


CiteScore 2018: 1.03

SCImago Journal Rank (SJR) 2018: 0.188
Source Normalized Impact per Paper (SNIP) 2018: 0.533

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2191-026X
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Analogy-Based Approaches to Improve Software Project Effort Estimation Accuracy

V Resmi
  • Corresponding author
  • Department of Computer Applications, Udaya School of Engineering, Vellamodi, Tamil Nadu, 629 204, India
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/ S Vijayalakshmi
  • Department of Computer Applications, Thiagarajar College of Engineering, Madurai, Tamil Nadu, 625 015, India
  • Other articles by this author:
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Published Online: 2019-06-27 | DOI: https://doi.org/10.1515/jisys-2019-0023

Abstract

In the discipline of software development, effort estimation renders a pivotal role. For the successful development of the project, an unambiguous estimation is necessitated. But there is the inadequacy of standard methods for estimating an effort which is applicable to all projects. Hence, to procure the best way of estimating the effort becomes an indispensable need of the project manager. Mathematical models are only mediocre in performing accurate estimation. On that account, we opt for analogy-based effort estimation by means of some soft computing techniques which rely on historical effort estimation data of the successfully completed projects to estimate the effort. So in a thorough study to improve the accuracy, models are generated for the clusters of the datasets with the confidence that data within the cluster have similar properties. This paper aims mainly on the analysis of some of the techniques to improve the effort prediction accuracy. Here the research starts with analyzing the correlation coefficient of the selected datasets. Then the process moves through the analysis of classification accuracy, clustering accuracy, mean magnitude of relative error and prediction accuracy based on some machine learning methods. Finally, a bio-inspired firefly algorithm with fuzzy analogy is applied on the datasets to produce good estimation accuracy.

Keywords: Effort estimation; analogy-based estimation; classification; clustering; firefly optimization; fuzzy analogy; linear regression; multilayer perceptron; k-means algorithm; EM algorithm

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About the article

Received: 2019-01-15

Accepted: 2019-05-22

Published Online: 2019-06-27


Citation Information: Journal of Intelligent Systems, 20190023, ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, DOI: https://doi.org/10.1515/jisys-2019-0023.

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