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

fYear

2001

fDate

2001

Firstpage

470

Lastpage

473

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Systems and Applications, ACS/IEEE International Conference on. 2001

Conference_Location

Beirut

Print_ISBN

0-7695-1165-1

Type

conf

DOI

10.1109/AICCSA.2001.934046

Filename

934046