Title :
Prediction of software reliability: a comparison between regression and neural network non-parametric models
Author :
Aljahdali, Sultan H. ; Sheta, Alaa ; Rine, David
Author_Institution :
Sch. of Inf. Tech., George Mason Univ., Fairfax, VA, USA
Abstract :
In this paper, neural networks have been proposed as an alternative technique to build software reliability growth models. A feedforward 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 were used. A comparison between regression parametric models and neural network models is provided
Keywords :
computer aided software engineering; feedforward neural nets; nonparametric statistics; program debugging; program testing; software reliability; statistical analysis; feedforward neural net; neural network nonparametric models; predictive capability evaluation; program fault prediction; regression parametric models; software reliability growth models; software reliability prediction; software test/debug process; Application software; Artificial neural networks; Computer science; Equations; Feedforward neural networks; Neural networks; Parametric statistics; Predictive models; Software reliability; Software testing;
Conference_Titel :
Computer Systems and Applications, ACS/IEEE International Conference on. 2001
Conference_Location :
Beirut
Print_ISBN :
0-7695-1165-1
DOI :
10.1109/AICCSA.2001.934046
