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Deep Learning Approach Based on Fault Correction Time for Reliability Assessment of Cloud and Edge Open Source Software

In: Predictive Analytics in System Reliability

Author

Listed:
  • Hironobu Sone

    (IBM Japan, Ltd.)

  • Shoichiro Miyamoto

    (Yamaguchi University)

  • Yukinobu Kashihara

    (Tokyo City University)

  • Yoshinobu Tamura

    (Yamaguchi University)

  • Shigeru Yamada

    (Tottori University)

Abstract

We discuss a method of machine learning in order to consider the characteristics reliability trends of edge open source project. Then, we focus on the method based on deep learning analysis. Thereby, the proposed method will be able to extract the characteristics data in order to comprehend the trend of fault big data recorded on the bug tracking system in edge open source project. Moreover, several numerical examples are shown by using actual fault big data in the edge open source project. Then, the illustrative results based on the deep learning are shown by using our methods discussed in this chapter. We discuss that our method by deep learning and prediction model are useful to assess the quality and reliability of the edge open source project.

Suggested Citation

  • Hironobu Sone & Shoichiro Miyamoto & Yukinobu Kashihara & Yoshinobu Tamura & Shigeru Yamada, 2023. "Deep Learning Approach Based on Fault Correction Time for Reliability Assessment of Cloud and Edge Open Source Software," Springer Series in Reliability Engineering, in: Vijay Kumar & Hoang Pham (ed.), Predictive Analytics in System Reliability, pages 1-17, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-05347-4_1
    DOI: 10.1007/978-3-031-05347-4_1
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