A survey of software reliability growth models using non-parametric methods

• Published in: 2014 IEEE International Conference on Computational Intelligence and Computing Research pp. 1 - 5
• Main Authors: Saley, M. K., Sreedharan, Sasikumaran
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2014
• Subjects: Artificial Neural Networks; Fuzzy Neural Network; Multi-Layer Perceptron; Recurrent Neural Network; Software Reliability Growth Model
• DOI: 10.1109/ICCIC.2014.7238416
• ISBN: 1479939749; 9781479939749

In this paper, we explore the different approaches of non-parametric models to predict the software reliability. Software reliability is an important part of software quality assessment. Even though many conventional statistical models are successfully used to predict software reliability, no single model can apply in all situations. Software reliability prediction is hard to achieve. In order to improve the accuracy of software reliability prediction, non-parametric methods are suggested. Recently many research works are going on with the combination of Artificial Neural Networks, Fuzzy Logic and Genetic Algorithm. This survey paper explains the different approaches of the non-parametric ANN method to improve the reliability prediction.