Estimating the parameters of software reliability growth models using hybrid DEO-ANN algorithm

Abstract

The parameter estimation of software reliability growth model (SRGM) is extremely useful for software developers and has been broadly acknowledged and applied. However, each SRGM contains some undetermined parameters and estimation of these parameters is a fundamental task. Mostly, these parameters are estimated by the least square estimation (LSE) or the maximum likelihood estimation (MLE). However, these methods impose certain constraints on the parameter estimation of SRGM like requiring the modelling function. In this paper, we propose an efficient approach to estimate the parameters of SRGM using a hybrid dolphin echolocation optimisation-artificial neural network (DEO-ANN) through parallel computation. The DEO is utilise to optimise the weights and the structure of the ANN to reduce computational complexity. The performance of the proposed approach for parameter estimation of SRGM is also compared with other existing approaches. The experimental results show that the proposed parameter estimation approach using DEO-ANN is very effective and flexible, and the better software reliability growth performance can be obtained on the different software failure datasets.