Bayesian Regularization in a Neural Network Model to Estimate Lines of Code Using Function Points
K. K. Aggarwal, Yogesh Singh, Pravin Chandra and Manimala Puri
DOI : 10.3844/jcssp.2005.505.509
Journal of Computer Science
Volume 1, Issue 4
It is a well known fact that at the beginning of any project, the software industry needs to know, how much will it cost to develop and what would be the time required ? . This paper examines the potential of using a neural network model for estimating the lines of code, once the functional requirements are known. Using the International Software Benchmarking Standards Group (ISBSG) Repository Data (release 9) for the experiment, this paper examines the performance of back propagation feed forward neural network to estimate the Source Lines of Code. Multiple training algorithms are used in the experiments. Results demonstrate that the neural network models trained using Bayesian Regularization provide the best results and are suitable for this purpose.
© 2005 K. K. Aggarwal, Yogesh Singh, Pravin Chandra and Manimala Puri. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.