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Erschienen in:
2024 | OriginalPaper | Buchkapitel
Comparison of OSS Reliability Assessment Methods by Using Wiener Data Preprocessing Based on Deep Learning
verfasst von : Yoshinobu Tamura, Shoichiro Miyamoto, Lei Zhou, Shigeru Yamada
Erschienen in: Reliability Engineering for Industrial Processes
Verlag: Springer Nature Switzerland
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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.