GVSEE: A Global Village Service Effort Estimator to Estimate Software Services Development Effort

As software services have become a main and basic part of companies in recent years, accurate and efficient estimates of required effort for their development has turned into a major concern. Furthermore, the great variety, complexity, nonnormality, and inconsistency of software services have made e...
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Autor*in:	Bardsiri, Amid Khatibi [verfasserIn] Hashemi, Seyyed Mohsen Razzazi, Mohammadreza

Format:	Artikel
Sprache:	Englisch

Erschienen:	2016

Rechteinformationen:	Nutzungsrecht: © 2016 Taylor & Francis 2016

Schlagwörter:	Accuracy Software services Appointments & personnel changes Outsourcing

Übergeordnetes Werk:	Enthalten in: Applied artificial intelligence - New York, NY : Hemisphere Publ. Corp., 1987, 30(2016), 5, Seite 396
Übergeordnetes Werk:	volume:30 ; year:2016 ; number:5 ; pages:396

Links:	Volltext Link aufrufen Link aufrufen

DOI / URN:	10.1080/08839514.2016.1185858

Katalog-ID:	OLC1978070098

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author-letter	Bardsiri, Amid Khatibi
doi_str_mv	10.1080/08839514.2016.1185858
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title_sort	gvsee: a global village service effort estimator to estimate software services development effort
title_auth	GVSEE: A Global Village Service Effort Estimator to Estimate Software Services Development Effort
abstract	As software services have become a main and basic part of companies in recent years, accurate and efficient estimates of required effort for their development has turned into a major concern. Furthermore, the great variety, complexity, nonnormality, and inconsistency of software services have made estimation of the needed development effort a very difficult task. In spite of the numerous studies conducted, and improvements made, in the past, no single model has yet been introduced that can reliably estimate the required effort. All the proposed methods enjoy suitable performance under specific conditions but lack satisfactory accuracy in a general and global space. Therefore, apparently, it is impossible to introduce a global and efficient model for all types of services. This article proposes a new model called GVSEE that emphasizes the idea "Think locally, act globally." Unlike previous studies, this model does not rely on a specific method and, in addition to combining methods, takes a local look at each software service with the help of fuzzy clustering. The model was evaluated on the real dataset ISBSG and on two artificial datasets, and results obtained indicate its tangible efficiency and the lack of accuracy of other models. As well as its greater accuracy, other advantages of the proposed model over other models are its adaptability and flexibility in confronting complexities and uncertainties present in the area of software services.
abstractGer	As software services have become a main and basic part of companies in recent years, accurate and efficient estimates of required effort for their development has turned into a major concern. Furthermore, the great variety, complexity, nonnormality, and inconsistency of software services have made estimation of the needed development effort a very difficult task. In spite of the numerous studies conducted, and improvements made, in the past, no single model has yet been introduced that can reliably estimate the required effort. All the proposed methods enjoy suitable performance under specific conditions but lack satisfactory accuracy in a general and global space. Therefore, apparently, it is impossible to introduce a global and efficient model for all types of services. This article proposes a new model called GVSEE that emphasizes the idea "Think locally, act globally." Unlike previous studies, this model does not rely on a specific method and, in addition to combining methods, takes a local look at each software service with the help of fuzzy clustering. The model was evaluated on the real dataset ISBSG and on two artificial datasets, and results obtained indicate its tangible efficiency and the lack of accuracy of other models. As well as its greater accuracy, other advantages of the proposed model over other models are its adaptability and flexibility in confronting complexities and uncertainties present in the area of software services.
abstract_unstemmed	As software services have become a main and basic part of companies in recent years, accurate and efficient estimates of required effort for their development has turned into a major concern. Furthermore, the great variety, complexity, nonnormality, and inconsistency of software services have made estimation of the needed development effort a very difficult task. In spite of the numerous studies conducted, and improvements made, in the past, no single model has yet been introduced that can reliably estimate the required effort. All the proposed methods enjoy suitable performance under specific conditions but lack satisfactory accuracy in a general and global space. Therefore, apparently, it is impossible to introduce a global and efficient model for all types of services. This article proposes a new model called GVSEE that emphasizes the idea "Think locally, act globally." Unlike previous studies, this model does not rely on a specific method and, in addition to combining methods, takes a local look at each software service with the help of fuzzy clustering. The model was evaluated on the real dataset ISBSG and on two artificial datasets, and results obtained indicate its tangible efficiency and the lack of accuracy of other models. As well as its greater accuracy, other advantages of the proposed model over other models are its adaptability and flexibility in confronting complexities and uncertainties present in the area of software services.
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title_short	GVSEE: A Global Village Service Effort Estimator to Estimate Software Services Development Effort
url	http://dx.doi.org/10.1080/08839514.2016.1185858 http://www.tandfonline.com/doi/abs/10.1080/08839514.2016.1185858 http://search.proquest.com/docview/1798769603
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author2	Hashemi, Seyyed Mohsen Razzazi, Mohammadreza
author2Str	Hashemi, Seyyed Mohsen Razzazi, Mohammadreza
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doi_str	10.1080/08839514.2016.1185858
up_date	2024-07-03T20:30:41.859Z
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