Name
Abstract | ||
|---|---|---|
In the past 35 years more than 100 software reliability models are proposed. Most of them are parametric models. In this paper we present a comparative study of different non-parametric models based on the neural networks and regression model learned by the real coded genetic algorithm to predict the cumulative failure in the software. Experimental results show that the training of different models by our real coded genetic algorithm have a good predictive capability across different projects. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ITNG.2011.15 | Information Technology: New Generations |
Keywords | Field | DocType |
comparative study,different non-parametric model,different model,neural network,different project,genetic algorithm,software reliability model,cumulative failure modeling,evolutionary prediction,cumulative failure,good predictive capability,cumulant,neural nets,regression model,neural networks,artificial neural network,software reliability,regression analysis,artificial neural networks,genetic algorithms,parametric model,testing | Parametric model,Regression analysis,Computer science,Software,Artificial intelligence,Software quality,Artificial neural network,Machine learning,Genetic algorithm | Conference |
ISBN | Citations | PageRank |
978-0-7695-4367-3 | 0 | 0.34 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Benaddy | 1 | 0 | 0.34 |
| Sultan Aljahdali | 2 | 71 | 15.26 |
| Mohamed Wakrim | 3 | 2 | 3.47 |