Genetic Algorithm for optimizing functional link artificial neural network based software cost estimation

Abstract

As Software becomes more complex and its scope dynamically increases, the importance of research on developing methods for estimating software development efforts has perpetually increased. Such accurate estimation has a prominent impact on the success of projects.The proposed work uses Functional Link neural network (FLANN) based estimation, which is essentially a machine learning approach, is one of the most popular techniques. In this paper the author has proposed a 2 step process for software effort prediction. In first phase known as training phase the FLANN selects the matching class (datasets) for the given input, which is improved by optimizing the parameters of each individual dataset by Genetic algorithm. In second step known as testing phase, the prediction process is done by Functional Link Artificial Neural Networks. The proposed method uses COCOMO-II as base model. The experimental results show that our method could significantly improve prediction accuracy of conventional Functional Link Artificial Neural Networks (FLANN) and has potential to become an effective method for software cost estimation. © 2012 Springer-Verlag GmbH Berlin Heidelberg.