Abstract
This chapter focuses on the comparison of the methods of open source software (OSS) reliability assessment. The fault detection phenomenon depends on the reporter and the severity, because the number of software fault is influenced by the reporter, severity, assignee, and component, etc. Actually, the software reliability growth models with testing-effort have been proposed in the past. In this chapter, we apply the deep learning approach to the OSS fault big data. Then, we show several reliability assessment measures based on the reporter and severity by using the the deep learning. Moreover, several numerical illustrations based on the proposed deep learning model and the data preprocessing are shown in this chapter.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ibrahim IM et al (2018) A robust generic multi-authority attributes management system for cloud storage services. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2018.2867871
Ahmad AA et al (2019) Scalability analysis comparisons of cloud-based software services. J Cloud Comput Adv Syst Appl.https://doi.org/10.1186/s13677-019-0134-y
Ozcan MO, Odaci F, Ari I (2019) Remote Debugging for containerized applications in edge computing environments. Proceedings of the 2019 IEEE international conference on edge computing (EDGE), Milan, Italy, pp 30–32. https://doi.org/10.1109/EDGE.2019.00021
Ngoko Y, Cerin C (2017) An edge computing platform for the detection of acoustic events. Proceedings of the 2017 IEEE international conference on edge computing (EDGE), Honolulu, HI, USA, pp 240–243. https://doi.org/10.1109/IEEE.EDGE.2017.44
Caprolu M, Di Pietro R, Lombardi F, Raponi S (2019) Edge computing perspectives: architectures, technologies, and open security issues. Proceedings of the 2019 IEEE international conference on edge computing (EDGE), Milan, Italy, pp 116–123. https://doi.org/10.1109/EDGE.2019.00035
Dolui K, Datta SK (2017) Comparison of edge computing implementations: fog computing, cloudlet and mobile edge computing. Proceedings of the 2017 global internet of things summit (GIoTS), Geneva, Switzerland, pp 1–6. https://doi.org/10.1109/GIOTS.2017.8016213
Yamada S, Tamura Y (2016) OSS reliability measurement and assessment. Springer International Publishing, Switzerland
Yamada S (2014) Software reliability modeling: fundamentals and applications. Springer, Tokyo/Heidelberg
Lyu MR (ed) (1996) Handbook of software reliability engineering. IEEE Computer Society Press, Los Alamitos, CA, U.S.A.
Musa JD, Iannino A, Okumoto K (1987) Software reliability: measurement, prediction, application. McGraw–Hill, New York
Kapur PK, Pham H, Gupta A, Jha PC (2011) Software reliability assessment with OR applications. Springer, London
The OpenStack project, Build the future of Open Infrastructure. https://www.openstack.org/
Acknowledgements
This work was supported in part by the JSPS KAKENHI Grant No. 23K11066 in Japan.
Funding
This study was funded by the JSPS KAKENHI Grant No. 23K11066 in Japan.
Conflict of InterestThe authors declare that they have no conflict of interest.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Tamura, Y., Miyamoto, S., Zhou, L., Yamada, S. (2024). Comparison of OSS Reliability Assessment Methods by Using Wiener Data Preprocessing Based on Deep Learning. In: Kapur, P.K., Pham, H., Singh, G., Kumar, V. (eds) Reliability Engineering for Industrial Processes. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-55048-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-55048-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-55047-8
Online ISBN: 978-3-031-55048-5
eBook Packages: EngineeringEngineering (R0)