Open AccessBayesian Regularization in a Neural Network Model to Estimate Lines of Code Using Function Points
Author(s) -
K. K. Aggarwal,
Yogesh Singh,
P. Chandra,
Manimala Puri
Publication year - 2005
Publication title -
journal of computer sciences/journal of computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.161
H-Index - 28
eISSN - 1552-6607
pISSN - 1549-3636
DOI - 10.3844/jcssp.2005.505.509
Subject(s) - computer science , artificial neural network , regularization (linguistics) , code (set theory) , bayesian probability , artificial intelligence , bayesian network , function point , algorithm , function (biology) , pattern recognition (psychology) , machine learning , data mining , set (abstract data type) , software , evolutionary biology , biology , software development , programming language
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