DocumentCode :
3579194
Title :
A survey of software reliability growth models using non-parametric methods
Author :
Saley, M.K. ; Sreedharan, Sasikumaran
Author_Institution :
Manonmaniam Sundaranar University, Thirunelveli, India
fYear :
2014
Firstpage :
1
Lastpage :
5
Abstract :
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.
Keywords :
Artificial Neural Networks; Fuzzy Neural Network; Multi-Layer Perceptron; Recurrent Neural Network; Software Reliability Growth Model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Computing Research (ICCIC), 2014 IEEE International Conference on
Print_ISBN :
978-1-4799-3974-9
Type :
conf
DOI :
10.1109/ICCIC.2014.7238416
Filename :
7238416
Link To Document :
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