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

被引：34

作者：

Aljahdali, SH ^{[1
]}

Sheta, A ^{[1
]}

Rine, D ^{[1
]}

机构：

[1] George Mason Univ, Sch Informat Tech, Fairfax, VA 22030 USA

来源：

ACS/IEEE INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS, PROCEEDINGS | 2001年

关键词：

D O I：

10.1109/AICCSA.2001.934046

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 [1]. A comparison between regression parametric models and neural network models is provided.

引用

页码：470 / 473

页数：2

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