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International Society of Science and Applied Technologies |
| OSS Reliability Assessment Method Based on Deep Learning and Two Dimensional Noisy Processes | ||||
| Author | Yoshinobu Tamura
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| Co-Author(s) | Shoichiro Miyamoto; Lei Zhou; Shigeru Yamada
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| Abstract | The fault big data of open source software (OSS) is opened all over the world. Especially, the fault detection phenomenon depends on various situation of development style in OSS. Actually, many software reliability growth models have been energetically proposed by several researchers in the past. In this paper, we apply the deep learning approach to the OSS fault big data. Then, we propose several reliability assessment measures based on the deep learning. As an approach, the range of estimate expands by the Wiener process embedded for the data preprocessing. Furthermore, this paper proposes the performability as novel reliability assessment measure from the proposed deep learning model. Several numerical examples based on the proposed deep learning model by using the actual fault big data sets are shown in this paper.
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| Keywords | Open source software, deep learning, reliability, Wiener process, performability | |||
| Article #: RQD2024-219 | ||||
Proceedings of 29th ISSAT International Conference on Reliability & Quality in Design |