TY  - JOUR
AU  - Aggarwal, K. K. 
AU  - Singh, Yogesh 
AU  - Chandra, Pravin 
AU  - Puri, Manimala 
PY  - 2005
TI  - Bayesian Regularization in a Neural Network Model to Estimate Lines of Code Using Function Points 
JF  - Journal of Computer Science
VL  - 1
IS  - 4
DO  - 10.3844/jcssp.2005.505.509
UR  - https://thescipub.com/abstract/jcssp.2005.505.509
AB  - 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.