Neuro-Fuzzy Evaluation of the Software Reliability Models by Adaptive Neuro Fuzzy Inference System

被引：0

作者：

Milovancevic, Milos ^{[1
]}

Dimov, Aleksandar ^{[2
]}

Spasov, Kamen Boyanov ^{[2
]}

Vracar, Ljubomir ^{[3
]}

Planic, Miroslav ^{[4
]}

机构：

[1] Univ Nis, Fac Mech Engn, Aleksandra Medvedeva 14, Nish 18000, Serbia

[2] St Kliment Ohridski Univ Sofia, Fac Math & Informat, Sofia, Bulgaria

[3] Univ Nis, Fac Elect Engn, Aleksandra Medvedeva 14, Nish 18000, Serbia

[4] Business & Law Fac, Belgrade, Serbia

来源：

JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS | 2021年 / 37卷 / 04期

关键词：

Neuro-fuzzy; Software reliability; Different scenarios; PREDICTION; UNCERTAINTY;

D O I：

10.1007/s10836-021-05964-y

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Software quality has become a key aspect of any electronic system. In this respect, software reliability is an important quality characteristic and there are many models that aim to estimate the reliability from different perspectives. However, there are no industry established reliability models. There is need to estimate which reliability model has the best performance. In this study several reliability models are analyzed by a soft computing approach, called adaptive neuro-fuzzy inference system (neuro-fuzzy), in order to estimate the models' capability based on root mean square errors (RMSE). Various aspects of accuracy of some of the well-known software reliability models have been used in this work. According to the results Non-Homogeneous Poisson Process Model (NHPP) is the best software reliability model. A combination of Linear Littlewood-Verall (LV) and NHPP is the optimal combination of two software reliability models. In other words, the best results could be achieved if one combines the LV and NHPP models.

引用

页码：439 / 452

页数：14

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