A Comparison of Neural Network Model and Regression Model Approaches Based on Sub-functional Components

Authors:
Seçkin Tunalilar;Onur Demirors
Affiliations:
ASELSAN MGEO Division,;Informatics Institute, Middle East Technical University,
Venue:
IWSM '09 /Mensura '09 Proceedings of the International Conferences on Software Process and Product Measurement
Year:
2009

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Abstract

A number of models have been proposed to build a relationship between effort and software size, however we still do have difficulties for effort estimation. ANN and Regression models are two modeling approaches for effort estimation. In this study we investigated whether considering sub-components of sizing methods increase the accuracy of prediction of effort on ANN and Regression models. Our effort models were built by utilizing "sub-components of Cosmic Functional Size". Besides these subcomponents, "application type" is also considered as input for these models to analyze its effect on effort estimation. We also studied the functional similarity concept by examining its effect on improving the accuracy of these models. The dataset consist of 18 completed projects of the same organization.