Multifunctional optimized group method data handling for software effort estimation

Nowadays, the trend of significant effort estimations is in demand. Due to its popularity, the stakeholder needs effective and efficient software development processes with the best estimation and accuracy to suit all data types. Nevertheless, finding the best effort estimation model with good accur...

全面介绍

Saved in:

书目详细资料
主要作者:	Arbain, Siti Hajar
格式:	Thesis
语言:	English
出版:	2022
主题:	QA75 Electronic computers Computer science
在线阅读:	http://eprints.utm.my/id/eprint/101491/1/SitiHajarArbainPSC2022.pdf.pdf
标签:	添加标签没有标签, 成为第一个标记此记录!

id	my-utm-ep.101491
record_format	uketd_dc
spelling	my-utm-ep.1014912023-06-21T10:17:58Z Multifunctional optimized group method data handling for software effort estimation 2022 Arbain, Siti Hajar QA75 Electronic computers. Computer science Nowadays, the trend of significant effort estimations is in demand. Due to its popularity, the stakeholder needs effective and efficient software development processes with the best estimation and accuracy to suit all data types. Nevertheless, finding the best effort estimation model with good accuracy is hard to serve this purpose. Group Method of Data Handling (GMDH) algorithms have been widely used for modelling and identifying complex systems and potentially applied in software effort estimation. However, there is limited study to determine the best architecture and optimal weight coefficients of the transfer function for the GMDH model. This study aimed to propose a hybrid multifunctional GMDH with Artificial Bee Colony (GMDH-ABC) based on a combination of four individual GMDH models, namely, GMDH-Polynomial, GMDH-Sigmoid, GMDH-Radial Basis Function, and GMDH-Tangent. The best GMDH architecture is determined based on L9 Taguchi orthogonal array. Five datasets (i.e., Cocomo, Dershanais, Albrecht, Kemerer and ISBSG) were used to validate the proposed models. The missing values in the dataset are imputed by the developed MissForest Multiple imputation method (MFMI). The Mean Absolute Percentage Error (MAPE) was used as performance measurement. The result showed that the GMDH-ABC model outperformed the individual GMDH by more than 50% improvement compared to standard conventional GMDH models and the benchmark ANN model in all datasets. The Cocomo dataset improved by 49% compared to the conventional GMDH-LSM. Improvements of 71%, 63%, 67%, and 82% in accuracy were obtained for the Dershanis dataset, Albrecht dataset, Kemerer dataset, and ISBSG dataset, respectively, as compared with the conventional GMDH-LSM. The results indicated that the proposed GMDH-ABC model has the ability to achieve higher accuracy in software effort estimation. 2022 Thesis http://eprints.utm.my/id/eprint/101491/ http://eprints.utm.my/id/eprint/101491/1/SitiHajarArbainPSC2022.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150562 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Arbain, Siti Hajar Multifunctional optimized group method data handling for software effort estimation
description	Nowadays, the trend of significant effort estimations is in demand. Due to its popularity, the stakeholder needs effective and efficient software development processes with the best estimation and accuracy to suit all data types. Nevertheless, finding the best effort estimation model with good accuracy is hard to serve this purpose. Group Method of Data Handling (GMDH) algorithms have been widely used for modelling and identifying complex systems and potentially applied in software effort estimation. However, there is limited study to determine the best architecture and optimal weight coefficients of the transfer function for the GMDH model. This study aimed to propose a hybrid multifunctional GMDH with Artificial Bee Colony (GMDH-ABC) based on a combination of four individual GMDH models, namely, GMDH-Polynomial, GMDH-Sigmoid, GMDH-Radial Basis Function, and GMDH-Tangent. The best GMDH architecture is determined based on L9 Taguchi orthogonal array. Five datasets (i.e., Cocomo, Dershanais, Albrecht, Kemerer and ISBSG) were used to validate the proposed models. The missing values in the dataset are imputed by the developed MissForest Multiple imputation method (MFMI). The Mean Absolute Percentage Error (MAPE) was used as performance measurement. The result showed that the GMDH-ABC model outperformed the individual GMDH by more than 50% improvement compared to standard conventional GMDH models and the benchmark ANN model in all datasets. The Cocomo dataset improved by 49% compared to the conventional GMDH-LSM. Improvements of 71%, 63%, 67%, and 82% in accuracy were obtained for the Dershanis dataset, Albrecht dataset, Kemerer dataset, and ISBSG dataset, respectively, as compared with the conventional GMDH-LSM. The results indicated that the proposed GMDH-ABC model has the ability to achieve higher accuracy in software effort estimation.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Arbain, Siti Hajar
author_facet	Arbain, Siti Hajar
author_sort	Arbain, Siti Hajar
title	Multifunctional optimized group method data handling for software effort estimation
title_short	Multifunctional optimized group method data handling for software effort estimation
title_full	Multifunctional optimized group method data handling for software effort estimation
title_fullStr	Multifunctional optimized group method data handling for software effort estimation
title_full_unstemmed	Multifunctional optimized group method data handling for software effort estimation
title_sort	multifunctional optimized group method data handling for software effort estimation
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Computing
publishDate	2022
url	http://eprints.utm.my/id/eprint/101491/1/SitiHajarArbainPSC2022.pdf.pdf
_version_	1776100710613516288

Multifunctional optimized group method data handling for software effort estimation

相似书籍