Applying a Feedforward Neural Network for Predicting Software Development Effort of Short-Scale Projects

Authors:
Ivica Kalichanin-Balich;Cuauhtemoc Lopez-Martin
Affiliations:
-;-
Venue:
SERA '10 Proceedings of the 2010 Eighth ACIS International Conference on Software Engineering Research, Management and Applications
Year:
2010

Citing 0
Cited 2

Comparison of regression model, feed-forward neural network and radial basis neural network for software development effort estimation

ACM SIGSOFT Software Engineering Notes
Software development effort estimation in academic environments applying a general regression neural network involving size and people factors

MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition

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Abstract

The software project effort estimation is an important aspect of software engineering practices. The improvement in accuracy of estimations is a topic that still remains as one of the greatest challenges of software engineering and computer science in general. In this work, the effort estimation for shortscale software projects, developed in academic setting, is modeled by two techniques: statistical regression and neural network. Two groups of software projects were made. One group of projects was used to calculate linear regression parameters and to train a neural network. The two models were then compared on both groups, the one used for their calculation and the other that was not used before. The accuracy of estimates was measured by using the magnitude of error relative to the estimate (MER) for each project and its mean MMER over each group of projects. The hypothesis accepted in this paper suggested that a feed forward neural network could be used for predicting short-scale software projects.