Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software
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 pr...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ram Arti Publishers
2023-08-01
|
| Series: | International Journal of Mathematical, Engineering and Management Sciences |
| Subjects: | |
| Online Access: | https://www.ijmems.in/article_detail.php?vid=8&issue_id=39&article_id=507 |
| _version_ | 1797795934348771328 |
|---|---|
| author | Yoshinobu Tamura Shigeru Yamada |
| author_facet | Yoshinobu Tamura Shigeru Yamada |
| author_sort | Yoshinobu Tamura |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T03:26:37Z |
| format | Article |
| id | doaj.art-b5025c46a4a1461c9923c0de887e4b6d |
| institution | Directory Open Access Journal |
| issn | 2455-7749 |
| language | English |
| last_indexed | 2024-03-13T03:26:37Z |
| publishDate | 2023-08-01 |
| publisher | Ram Arti Publishers |
| record_format | Article |
| series | International Journal of Mathematical, Engineering and Management Sciences |
| spelling | doaj.art-b5025c46a4a1461c9923c0de887e4b6d2023-06-25T08:54:56ZengRam Arti PublishersInternational Journal of Mathematical, Engineering and Management Sciences2455-77492023-08-0184632639https://doi.org/10.33889/IJMEMS.2023.8.4.036Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source SoftwareYoshinobu Tamura0Shigeru Yamada1Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, Yamaguchi, Japan.Graduate School of Engineering, Tottori University, Tottori, Tottori, Japan.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.https://www.ijmems.in/article_detail.php?vid=8&issue_id=39&article_id=507open-source softwaredeep learningfine tuningsimilar open-source software |
| spellingShingle | Yoshinobu Tamura Shigeru Yamada 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 open-source software deep learning fine tuning similar open-source software |
| title | Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software |
| title_full | Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software |
| title_fullStr | Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software |
| title_full_unstemmed | Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software |
| title_short | Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software |
| title_sort | deep learning based on fine tuning with application to the reliability assessment of similar open source software |
| topic | open-source software deep learning fine tuning similar open-source software |
| url | https://www.ijmems.in/article_detail.php?vid=8&issue_id=39&article_id=507 |
| work_keys_str_mv | AT yoshinobutamura deeplearningbasedonfinetuningwithapplicationtothereliabilityassessmentofsimilaropensourcesoftware AT shigeruyamada deeplearningbasedonfinetuningwithapplicationtothereliabilityassessmentofsimilaropensourcesoftware |