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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th-cmuir.6653943832-411842017-09-28T04:18:45Z Software size estimation in design phase based on MLP neural network Panyangam B. Kiewkanya M. © 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. 2017-09-28T04:18:45Z 2017-09-28T04:18:45Z 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/6653943832/41184 |
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© 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 |
Panyangam B. Kiewkanya M. |
| spellingShingle |
Panyangam B. Kiewkanya M. Software size estimation in design phase based on MLP neural network |
| author_facet |
Panyangam B. Kiewkanya M. |
| author_sort |
Panyangam B. |
| 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 |
2017 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85022176707&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/41184 |
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