Effort estimation in agile software development using experimental validation of neural network models

Abstract

Frequent requirement changes are a major point of concern in today’s scenario. As a solution to such issues, agile software development (ASD) has efficiently replaced the traditional methods of software development in industries. Because of dynamics of different aspects of ASD, it is very difficult to keep track, maintain and estimate the overall product. So, in order to solve the effort estimation problem (EEP) in ASD, different types of artificial neural networks (ANNs) have been applied. This work focuses on two types of ANN-feedforward back-propagation neural network and Elman neural network. These two networks have been applied to a dataset which contains project information of 21 projects based on ASD from 6 different software houses to analyze and solve the EEP. Also, the proposed work uses three different performance metrics i.e. mean magnitude of relative error (MMRE), mean square error (MSE) and prediction (PRED(x)) to examine the performance of the model. The results of the proposed models are compared to the existing models in the literature.