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
Link To Document :
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