A New Approach to Software Effort Estimation Using Different Artificial Neural Network Architectures and Taguchi Orthogonal Arrays

In this article, two different architectures of Artificial Neural Networks (ANN) are proposed as an efficient tool for predicting and estimating software effort. Artificial Neural Networks, as a branch of machine learning, are used in estimation because they tend towards fast learning and giving bet...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.26926-26936
Main Authors: Rankovic, Nevena, Rankovic, Dragica, Ivanovic, Mirjana, Lazic, Ljubomir
Format: Article
Language:English
Subjects:
Artificial neural networks
artificial neural networks design
clustering
COCOMO2000
COCOMO81
Computer architecture
Convergence
Estimation
Kemerer dataset
Learning theory
Machine learning
NASA project dataset
Neural networks
Numerical models
Optimization
orthogonal array-based experiments
Orthogonal arrays
Robust design
Software
Software algorithms
Software development
Software effort estimation
Taguchi method
Taguchi methods
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Summary:In this article, two different architectures of Artificial Neural Networks (ANN) are proposed as an efficient tool for predicting and estimating software effort. Artificial Neural Networks, as a branch of machine learning, are used in estimation because they tend towards fast learning and giving better and more accurate results. The search/optimization embraced here is motivated by the Taguchi method based on Orthogonal Arrays (an extraordinary set of Latin Squares), which demonstrated to be an effective apparatus in a robust design. This article aims to minimize the magnitude relative error (MRE) in effort estimation by using Taguchi's Orthogonal Arrays, as well as to find the simplest possible architecture of an artificial Neural Network for optimized learning. A descending gradient (GA) criterion has also been introduced to know when to stop performing iterations. Given the importance of estimating software projects, our work aims to cover as many different values of actual efficiency of a wide range of projects as possible by division into clusters and a certain coding method, in addition to the mentioned tools. In this way, the risk of error estimation can be reduced, to increase the rate of completed software projects.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3057807