K-Means Clustering Algorithms to Compute Software Effort Estimation

Abstract

Software development effort estimation is the way of predicting the effort to improve software economics. Accurate estimation of effort is the most tedious tasks in software projects. However, several methods are used to estimate the software development effort accurately. Imprecise estimation can leads to project failure due to uncertain data. In this paper, a hybrid model based on combination of Particle Swarm Optimization (PSO), K-means clustering algorithms, neural network and ABE method is proposed. The proposed method can be useful to predict better clustering and more accurate estimation and hence, there are difficulties in clustering and outliers in the software projects. The obtained results showed the better clustering result which provides the estimation result accurately. Then, neural network and Analogy methods are used which enhance the accuracy significantly.