research-article

Application of feed-forward neural networks for software reliability prediction

Authors:

Pradeep KumarAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 35, Issue 5

Pages 1 - 6

https://doi.org/10.1145/1838687.1838709

Published: 22 October 2010 Publication History

Abstract

Many analytical models have been proposed for modeling software reliability growth trends with different predictive capabilities at different phases of testing yet there still is a need to develop a model that can be applied for accurate predictions in a realistic environment. In this paper we describe a software reliability prediction model using feed-forward neural network for better reliability prediction through back-propagation algorithm and discuss the issues of network architecture and data representation methods. We demonstrate a comparative analysis between the proposed approach and three well known software reliability growth prediction models using seven different failure datasets collected from standard software projects to test the validity of the presented method. A numerical example also has been cited to illustrate the results that revealed significant improvement by using Artificial Neural Network (ANN) over conventional statistical models based on NHPP.

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Cited By

FU NRYU DKIM S(2024)MuSRGM: A Genetic Algorithm-Based Dynamic Combinatorial Deep Learning Model for Software Reliability EngineeringIEICE Transactions on Information and Systems10.1587/transinf.2023EDP7183E107.D:6(761-771)Online publication date: 1-Jun-2024
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Sayal AJha JN CGupta VGupta AGupta OMemoria M(2023)Neural Networks And Machine Learning2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)10.1109/ICCCMLA58983.2023.10346612(58-63)Online publication date: 7-Oct-2023
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Rath SSahu MDas SPradhan J(2022)Survey on Machine Learning Techniques for Software Reliability Accuracy PredictionMeta Heuristic Techniques in Software Engineering and Its Applications10.1007/978-3-031-11713-8_5(43-55)Online publication date: 18-Oct-2022
https://doi.org/10.1007/978-3-031-11713-8_5
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Application of feed-forward neural networks for software reliability prediction

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Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 35, Issue 5

September 2010

134 pages

ISSN:0163-5948

DOI:10.1145/1838687

Issue’s Table of Contents

Copyright © 2010 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2010

Published in SIGSOFT Volume 35, Issue 5

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Cited By

FU NRYU DKIM S(2024)MuSRGM: A Genetic Algorithm-Based Dynamic Combinatorial Deep Learning Model for Software Reliability EngineeringIEICE Transactions on Information and Systems10.1587/transinf.2023EDP7183E107.D:6(761-771)Online publication date: 1-Jun-2024
https://doi.org/10.1587/transinf.2023EDP7183
Sayal AJha JN CGupta VGupta AGupta OMemoria M(2023)Neural Networks And Machine Learning2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)10.1109/ICCCMLA58983.2023.10346612(58-63)Online publication date: 7-Oct-2023
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https://doi.org/10.1007/978-3-031-11713-8_5
Kumar PWahid A(2020)Investigation of Software Reliability Prediction Using Statistical and Machine Learning MethodsCognitive Analytics10.4018/978-1-7998-2460-2.ch085(1640-1660)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-2460-2.ch085
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Atif MLatif SAhmad RKiani AQadir JBaig AIshibuchi HAbbas W(2019)Soft Computing Techniques for Dependable Cyber-Physical SystemsIEEE Access10.1109/ACCESS.2019.29203177(72030-72049)Online publication date: 2019
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Gusmanov KLeavens GGarcia APăsăreanu C(2018)On the adoption of neural networks in modeling software reliabilityProceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3236024.3275433(962-964)Online publication date: 26-Oct-2018
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