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Prediction of software reliability: a comparison between regression and neural network non-parametric models
Conference proceeding

Prediction of software reliability: a comparison between regression and neural network non-parametric models

S.H. Aljahdali, A. Sheta, D. Rine and IEEE COMPUTER SOCIETY
Proceedings ACS/IEEE International Conference on Computer Systems and Applications, Vol.2001-, pp.470-473
2001
DOI: https://doi.org/10.1109/AICCSA.2001.934046

Abstract

Application software Artificial neural networks Computer science Equations Feedforward neural networks Neural networks Parametric statistics Predictive models Software reliability Software testing
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.

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