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Comparison of OSS Reliability Assessment Methods by Using Wiener Data Preprocessing Based on Deep Learning

In: Reliability Engineering for Industrial Processes

Author

Listed:
  • Yoshinobu Tamura

    (Yamaguchi University)

  • Shoichiro Miyamoto

    (Yamaguchi University)

  • Lei Zhou

    (Yamaguchi University)

  • Shigeru Yamada

    (Tottori University)

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.

Suggested Citation

  • Yoshinobu Tamura & Shoichiro Miyamoto & Lei Zhou & Shigeru Yamada, 2024. "Comparison of OSS Reliability Assessment Methods by Using Wiener Data Preprocessing Based on Deep Learning," Springer Series in Reliability Engineering, in: P. K. Kapur & Hoang Pham & Gurinder Singh & Vivek Kumar (ed.), Reliability Engineering for Industrial Processes, pages 1-17, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-55048-5_1
    DOI: 10.1007/978-3-031-55048-5_1
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