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		<citationkey>TrontoSilvSant:2007:CoArNe</citationkey>
		<title>Comparison of Artificial Neural Network and Regression Models in Software Effort Estimation</title>
		<year>2007</year>
		<secondarytype>PRE CI</secondarytype>
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		<author>Tronto, Iris Fabiana Barcelos,</author>
		<author>Silva, José Demisio Simões da,</author>
		<author>Sant'Anna, Nilson,</author>
		<group>LAC-INPE-MCT-BR</group>
		<group>LAC-INPE-MCT-BR</group>
		<group>LAC-INPE-MCT-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>iris_barcelos@lac.inpe.br</electronicmailaddress>
		<electronicmailaddress>demisio@lac.inpe.br</electronicmailaddress>
		<electronicmailaddress>nilson@lac.inpe.br</electronicmailaddress>
		<conferencename>International Joint Conference on Neural Networks, (IJCNN).</conferencename>
		<conferencelocation>Orlando, Flórida</conferencelocation>
		<date>12-17 Apr.</date>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Poster Session</tertiarytype>
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		<abstract>Good practices in software project management are basic requirements for companies to stay in the market, because the effective project management leads to improvements in product quality and cost reduction. Fundamental measurements are the prediction of size, effort, resources, cost and time spent in the software development process. In this paper, predictive Artificial Neural Network (ANN) and Regression based models are investigated, aiming at establishing simple estimation methods alternatives. The results presented in this paper compare the performance of both methods and show that artificial neural networks are effective in effort estimation.</abstract>
		<area>COMP</area>
		<language>en</language>
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