Software size estimation in design phase based on MLP neural network

Software size estimation in design phase based on MLP neural network

© Springer International Publishing AG 2018. Size estimation is one of important processes related to success of software project management. This paper presents novel software size estimation model by using Multilayer Perceptron approach. Software size in terms of Lines of code is used as criterion...

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Main Authors: Benjamas Panyangam, Matinee Kiewkanya
Format: Book Series
Published: 2018
Subjects:
Computer Science
Engineering
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85022176707&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/58546
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Institution: Chiang Mai University
id th-cmuir.6653943832-58546
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spelling th-cmuir.6653943832-585462018-09-05T04:28:53Z Software size estimation in design phase based on MLP neural network Benjamas Panyangam Matinee Kiewkanya Computer Science Engineering © Springer International Publishing AG 2018. Size estimation is one of important processes related to success of software project management. This paper presents novel software size estimation model by using Multilayer Perceptron approach. Software size in terms of Lines of code is used as criterion variable. Structural complexity metrics are used as predictors. The metrics can be captured from a software design model named UML Class diagram. A high predictive ability of the model is shown with correlation coefficient measure. Moreover, four training algorithms; Levenberg-Marquardt, Scaled Conjugate Gradient, Broyden-Fletcher-Golfarb-Shanno and Bayesian Regularization, have been applied on the network for better estimation. The obtained results indicate the highest accuracy on the model with Bayesian Regularization algorithm. 2018-09-05T04:26:09Z 2018-09-05T04:26:09Z 2018-01-01 Book Series 21945357 2-s2.0-85022176707 10.1007/978-3-319-60663-7_8 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85022176707&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/58546
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
Engineering
spellingShingle Computer Science
Engineering
Benjamas Panyangam
Matinee Kiewkanya
Software size estimation in design phase based on MLP neural network
description © Springer International Publishing AG 2018. Size estimation is one of important processes related to success of software project management. This paper presents novel software size estimation model by using Multilayer Perceptron approach. Software size in terms of Lines of code is used as criterion variable. Structural complexity metrics are used as predictors. The metrics can be captured from a software design model named UML Class diagram. A high predictive ability of the model is shown with correlation coefficient measure. Moreover, four training algorithms; Levenberg-Marquardt, Scaled Conjugate Gradient, Broyden-Fletcher-Golfarb-Shanno and Bayesian Regularization, have been applied on the network for better estimation. The obtained results indicate the highest accuracy on the model with Bayesian Regularization algorithm.
format Book Series
author Benjamas Panyangam
Matinee Kiewkanya
author_facet Benjamas Panyangam
Matinee Kiewkanya
author_sort Benjamas Panyangam
title Software size estimation in design phase based on MLP neural network
title_short Software size estimation in design phase based on MLP neural network
title_full Software size estimation in design phase based on MLP neural network
title_fullStr Software size estimation in design phase based on MLP neural network
title_full_unstemmed Software size estimation in design phase based on MLP neural network
title_sort software size estimation in design phase based on mlp neural network
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85022176707&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/58546
_version_ 1681425085596958720

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