DocumentCode
1942652
Title
Comparison of Artificial Neural Network and Regression Models in Software Effort Estimation
Author
De Barcelos, Iris Fabiana Tronto ; Silva, José Demísio Simões da ; Sant´Anna, Nilson
Author_Institution
Brazilian Nat. Inst. for Space Res., Campos
fYear
2007
fDate
12-17 Aug. 2007
Firstpage
771
Lastpage
776
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.
Keywords
neural nets; project management; regression analysis; software management; artificial neural network; cost reduction; product quality; regression based models; regression models; software development process; software effort estimation; software project management; Accuracy; Artificial neural networks; Costs; Iris; Mathematical model; Predictive models; Programming; Project management; Size measurement; Software quality;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location
Orlando, FL
ISSN
1098-7576
Print_ISBN
978-1-4244-1379-9
Electronic_ISBN
1098-7576
Type
conf
DOI
10.1109/IJCNN.2007.4371055
Filename
4371055
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