Paper Info
Title
Prediction of Software Reliability: A Comparison between Regression and Neural Network Non-Parametric Models
Abstract
Abstract: In this paper neural networks have been proposed as an alternative technique to build software reliability growth models. A feed-forward neural network was used to predict the number of faults initially resident in a program at the beginning of a test/debug process. To evaluate the predictive capability of the developed model data sets from various projects w ere used [1]. A comparison between regression parametric models and neural network models is provided.
Year
DOI
Venue
2001
10.1109/AICCSA.2001.934046
AICCSA
Keywords
Field
DocType
artificial neural networks,computer aided software engineering,software reliability,neural networks,parametric model,feedforward neural networks,statistical analysis,neural network,feedforward neural network,nonparametric statistics,software testing,parametric statistics,predictive models,computer science,neural network model,application software
Data mining,Feedforward neural network,Parametric model,Computer science,Nonparametric statistics,Probabilistic neural network,Software reliability testing,Artificial intelligence,Computer-aided software engineering,Artificial neural network,Software quality,Machine learning
Conference
ISBN
Citations 
PageRank 
0-7695-1165-1
24
1.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Sultan Aljahdali17115.26
David Rine226445.40
Alaa F. Sheta313313.99