Artificial Neural Network Models to Predict Effort and Errors for Embedded Software Development Projects
Kazunori Iwata, Toyoshiro Nakashima, Yoshiyuki Anan, Naohiro Ishii · IEEJ Transactions on Electronics Information and Systems · 2010
In this paper, we establish effort and error prediction models using an artificial neural networks (ANNs). We propose the normalizing method to reduce the margin of errors for ANN models. In addition, we perform an evaluation experiment to compare the accuracy of the ANN models with that of the regression analysis (RA) model and that of two ANN models using Steel-Dwass's multiple comparison test. The results show that each ANN model is more accurate than the RA model and the proposed method can reduce the errors for some cases, since the mean errors of the ANN models are statistically significantly lower.