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International Society of Science and Applied Technologies |
| OSS Performability Analysis Based on Deep Multitask Learning as Multi-Objective Optimization | ||||
| Author | Yoshinobu Tamura
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| Co-Author(s) | Adarsh Anand; Pramod Kumar Kapur; Shigeru Yamada
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| Abstract | Recently, many deep learning methods have been used in various research areas. In particular, we will be able to obtain new knowledge by using the large scale data. We focus on the method of reliability assessment based on the deep learning. Then, this paper proposes the deep multitask learning by using the fault big data. Moreover, we propose the performability as the OSS assessment measure obtained from the deep multitask learning. Several numerical illustrations based on the proposed method are shown in this paper.
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| Keywords | Open source software, deep multitask learning, performability, software reliability | |||
| Article #: RQD2025-302 | ||||
Proceedings of 30th ISSAT International Conference on Reliability & Quality in Design |