Software Effort Estimation with Different Artificial Neural Network

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1572

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DC Field	Value	Language
dc.contributor.author	Singh, J	-
dc.contributor.author	Sahoo, Bibhudatta	-
dc.date.accessioned	2011-12-22T09:07:14Z	-
dc.date.available	2011-12-22T09:07:14Z	-
dc.date.issued	2011	-
dc.identifier.citation	IJCA Special Issue on 2nd National Conference- Computing, Communication and Sensor Network (CCSN) (4):13-17, 2011. Published by Foundation of Computer Science, New York, USA.	en
dc.identifier.uri	http://hdl.handle.net/2080/1572	-
dc.description	Copyright belongs to International Journal of Computer Applications	en
dc.description.abstract	Failures of software are mainly due to the faulty project management practices, which includes effort estimation. Continuous changing scenarios of software development technology makes effort estimation more challenging. Ability of ANN(Artificial Neural Network) to model a complex set of relationship between the dependent variable (effort) and the independent variables (cost drivers) makes it as a potential tool for estimation. This paper presents a performance analysis of different ANNs in effort estimation. We have simulated four types of ANN created by MATLAB10 NNTool using NASA dataset	en
dc.format.extent	785811 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	-
dc.publisher	Foundation of Computer Science, USA	en
dc.subject	Effort Estimation	en
dc.subject	Artificial Neural Network	en
dc.subject	NNtool	en
dc.subject	MMRE	en
dc.title	Software Effort Estimation with Different Artificial Neural Network	en
dc.type	Article	en
Appears in Collections:	Journal Articles

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