Table of Contents
Advances in Software Engineering
Volume 2014 (2014), Article ID 284531, 6 pages
http://dx.doi.org/10.1155/2014/284531
Research Article

Prediction Model for Object Oriented Software Development Effort Estimation Using One Hidden Layer Feed Forward Neural Network with Genetic Algorithm

1Department of Computer Science & Engineering, Noida Institute of Engineering & Technology, Greater Noida 201306, India
2Department of Computer Science & Engineering, Harcourt Butler Technological Institute, Kanpur 208002, India

Received 3 February 2014; Revised 6 May 2014; Accepted 13 May 2014; Published 3 June 2014

Academic Editor: Robert J. Walker

Copyright © 2014 Chandra Shekhar Yadav and Raghuraj Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The budget computation for software development is affected by the prediction of software development effort and schedule. Software development effort and schedule can be predicted precisely on the basis of past software project data sets. In this paper, a model for object-oriented software development effort estimation using one hidden layer feed forward neural network (OHFNN) has been developed. The model has been further optimized with the help of genetic algorithm by taking weight vector obtained from OHFNN as initial population for the genetic algorithm. Convergence has been obtained by minimizing the sum of squared errors of each input vector and optimal weight vector has been determined to predict the software development effort. The model has been empirically validated on the PROMISE software engineering repository dataset. Performance of the model is more accurate than the well-established constructive cost model (COCOMO).