基于深度学习考虑努力维纳过程数据的OSS可持续性评估

IF 0.9 Q3 ENGINEERING, MULTIDISCIPLINARY International Journal of Reliability Quality and Safety Engineering Pub Date : 2023-11-04 DOI:10.1142/s0218539323500328
Yoshinobu Tamura, Shoichiro Miyamoto, Lei Zhou, Shigeru Yamada
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引用次数: 0

摘要

本文重点研究了基于开源软件故障大数据的可持续性。故障检测现象取决于维护工作量,因为软件故障的数量受维护工作量的影响。实际上,过去已经提出了带有测试努力的软件可靠性增长模型。本文将深度学习方法应用于OSS故障大数据。同时,提出了可持续性可靠性评价指标。然后,我们给出了几种基于深度学习的可持续性评估方法。此外,本文还给出了基于所提出的深度学习模型的几个数值实例。
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OSS Sustainability Assessment Based on the Deep Learning Considering Effort Wiener Process Data
This paper focuses on the sustainability based on the effort by using the fault big data of open source software (OSS). The fault detection phenomenon depends on the maintenance effort, because the number of software fault is influenced by the effort expenditure. Actually, the software reliability growth models with testing-effort have been proposed in the past. In this paper, we apply the deep learning approach to the OSS fault big data. Also, we propose the reliability assessment measure of sustainability. Then, we show several sustainability assessment measure based on the deep learning. Moreover, several numerical illustrations based on the proposed deep learning model are shown in this paper.
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来源期刊
CiteScore
1.70
自引率
25.00%
发文量
26
期刊介绍: IJRQSE is a refereed journal focusing on both the theoretical and practical aspects of reliability, quality, and safety in engineering. The journal is intended to cover a broad spectrum of issues in manufacturing, computing, software, aerospace, control, nuclear systems, power systems, communication systems, and electronics. Papers are sought in the theoretical domain as well as in such practical fields as industry and laboratory research. The journal is published quarterly, March, June, September and December. It is intended to bridge the gap between the theoretical experts and practitioners in the academic, scientific, government, and business communities.
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