ヒト免疫システムを模倣したWeb3.0時代におけるクラウド・エッジ自動修復基盤の構築
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1.A NHPP-based SRGM considering the number of users for OSS
- 关键词：
- Computer software maintenance;Computer software selection and evaluation;Open source software;Software quality;UNIX;Estimation results;Mean value function;Model assumptions;Non-homogeneous Poisson process;Open-source softwares;Process-based;Software reliability growth models;Software updates;Software-Reliability;Tracking system
- Miyamoto, Shoichiro;Zhou, Lei;Tamura, Yoshinobu;Yamada, Shigeru
- 《International Journal of System Assurance Engineering and Management》
- 2025年
- 卷
- 期
- 期刊
Previously, many researchers proposed various software reliability growth model (SRGM) to evaluate software reliability. Also, they applied SRGM to open source software (OSS). On the other hand, almost all OSS continuing to develop. In this case, OSS’s latent faults fluctuates frequently. Moreover, it is difficult to evaluate accurately because this doesn’t consider by SRGM assumptions. It is known that as the number of software users increases, the number of the detected faults tends to increase. Especially, it is seen in like the number of the users changes fluctuates and the software update. In this paper, we analyze the impact of fluctuations in the number of software users on OSS using actual data from a fault tracking system. Moreover, we review the assumptions of non-homogeneous Poisson process (NHPP) based SRGM and propose the corrected time for model considering the number of number of users. Furthermore, we applied propose model to existing general NHPP-based SRGM’s mean value function. As a result, we confirmed that the number of users increases effect to the number of the detected faults. Furthermore, the propose model can evaluate software reliability accurately. Especially, logarithmic Poisson SRGM shows the highest estimation result. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025.
...2.Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software
- 关键词：
- Application programs;Deep learning;Learning systems;Open systems;Reliability analysis;Software design;Software reliability;Data set;Deep learning;Fault data;Fine tuning;Machine-learning;Open source products;Open-source softwares;Quality evaluation;Similar open-source software
- Tamura, Yoshinobu;Yamada, Shigeru
- 《International Journal of Mathematical, Engineering and Management Sciences》
- 2023年
- 8卷
- 4期
- 期刊
Recently, many open-source products have been used under the situations of general software development, because the cost saving and standardization. Therefore, many open-source products are gathering attention from many software development companies. Then, the reliability/quality of open-source products becomes very important factor for the software development. This paper focuses on the reliability/quality evaluation of open-source products. In particular, the large quantity fault data sets recorded on Bugzilla of open-source products is used in many open-source development projects. Then, the large amount of data sets of software faults is recorded on the Bugzilla. This paper proposes the reliability/quality evaluation approach based on the deep machine learning by using the large quantity fault data on the Bugzilla. Moreover, the large quantity fault data sets are analyzed by the deep machine learning based on the fine-tuning. copyright © International Journal of Mathematical, Engineering and Management Sciences.
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