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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Veröffentlicht in: | Applied artificial intelligence 2016-05, Vol.30 (5), p.396-428 |
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Sprache: | eng |
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Zusammenfassung: | 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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ISSN: | 0883-9514 1087-6545 |
DOI: | 10.1080/08839514.2016.1185858 |