Yoshinobu Tamura
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, Yamaguchi, Japan.

Shigeru Yamada
Graduate School of Engineering, Tottori University, Tottori, Tottori, Japan.

DOI https://doi.org/10.33889/IJMEMS.2023.8.4.036

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Abstract

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.

Keywords- Open-source software, Deep learning, Fine tuning, Similar open-source software.

Citation

Tamura, Y., & Yamada, S. (2023). Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software. International Journal of Mathematical, Engineering and Management Sciences, 8(4), 632-639. https://doi.org/10.33889/IJMEMS.2023.8.4.036.