%0 Electronic Source
%4 sid.inpe.br/ePrint@80/2006/12.20.23.38
%2 sid.inpe.br/ePrint@80/2006/12.20.23.38.24
%T The artificial neural networks model for software effort estimation
%8 2006-12-21
%9 On-line
%A Tronto, Iris Fabiana de Barcelos,
%A Silva, Jose Demisio Simoes da,
%A Anna, Nilson Sant,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress iris_barcelos@lac.inpe.br
%@electronicmailaddress demisio@lac.inpe.br
%@electronicmailaddress nilson@lac.inpe.br
%I Instituto
%I Nacional
%I de
%I Pesquisas
%I Espaciais
%C São José dos Campos
%K Software effort estimation, neural network model, regression analysis.
%X Machine learning techniques such as neural networks, rule induction, genetic algorithm and case-based reasoning are finding application in a wide variety of fields such as computer vision, econometrics and medicine, where human abilities have proven to be superior to those of computers. Such techniques hold the promise of being able to make sense of a variety of inputs of different types in producing an output. Software effort modeling has always appeared to be a rather hit-or-miss business where statistical methods frequently result in low accuracy of prediction. Some experiments using an artificial neural networks have been conducted, highlighting some of the problems that arise when machine learning techniques are applied to software effort modeling. These experiments show that, compared with conventional regression analysis, improved accuracy of prediction is possible.
%3 v1.pdf