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Erschienen in: Journal of Electronic Testing 4/2021

28.09.2021

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

verfasst von: Milos Milovancevic, Aleksandar Dimov, Kamen Boyanov Spasov, Ljubomir Vračar, Miroslav Planić

Erschienen in: Journal of Electronic Testing | Ausgabe 4/2021

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Abstract

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.

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Metadaten
Titel
Neuro-Fuzzy Evaluation of the Software Reliability Models by Adaptive Neuro Fuzzy Inference System
verfasst von
Milos Milovancevic
Aleksandar Dimov
Kamen Boyanov Spasov
Ljubomir Vračar
Miroslav Planić
Publikationsdatum
28.09.2021
Verlag
Springer US
Erschienen in
Journal of Electronic Testing / Ausgabe 4/2021
Print ISSN: 0923-8174
Elektronische ISSN: 1573-0727
DOI
https://doi.org/10.1007/s10836-021-05964-y

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