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

Yoshinobu Tamura (), Shoichiro Miyamoto (), Lei Zhou () and Shigeru Yamada ()
Additional contact information
Yoshinobu Tamura: Yamaguchi University
Shoichiro Miyamoto: Yamaguchi University
Lei Zhou: Yamaguchi University
Shigeru Yamada: Tottori University

A chapter in Reliability Engineering for Industrial Processes, 2024, pp 1-17 from Springer

Abstract: 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.

Keywords: Open source software; Deep learning; Reliability; Wiener data preprocessing (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-55048-5_1

Ordering information: This item can be ordered from
http://www.springer.com/9783031550485

DOI: 10.1007/978-3-031-55048-5_1

Access Statistics for this chapter

More chapters in Springer Series in Reliability Engineering from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().