OSS reliability assessment method based on deep learning and independent Wiener data preprocessing

Yoshinobu Tamura (), Shoichiro Miyamoto (), Lei Zhou (), Adarsh Anand (), P. K. Kapur () and Shigeru Yamada ()
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Yoshinobu Tamura: Yamaguchi University
Shoichiro Miyamoto: Yamaguchi University
Lei Zhou: Yamaguchi University
Adarsh Anand: University of Delhi
P. K. Kapur: Amity University
Shigeru Yamada: Tottori University

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 6, No 52, 2668-2676

Abstract: Abstract The fault big data sets of many open source software (OSS) are recorded on the bug tracking systems. In the past, we have proposed the effort assessment method under the assumption that the fault detection phenomenon depends on the maintenance effort, because the number of software fault is influenced by the effort expenditure. The past research in terms of the effort assessment method of OSS is based on the effort data sets. On the other hand, we propose the deep learning approach to the OSS fault big data. In the past, the existing method without Wiener process cannot estimate within the range of existing data only. The proposed method assumes that the fault detection process follows the Wiener process such as the imperfect debugging and Markov property. Thereby, the proposed method can estimate the exceeding values by adding the white noise based on the Wiener process. Then, the proposed method make it possible for the OSS managers to assess the values exceeding from the existing data. Then, we show several reliability assessment measures based on the fault modification time based on the deep learning. Moreover, several numerical illustrations based on the proposed deep learning model are shown in this paper.

Keywords: Deep learning; Wiener process; Fault modification time; Reliability; OSS (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-024-02288-w

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