Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software
Author:
Affiliation:
1. Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, Yamaguchi, Japan.
2. Graduate School of Engineering, Tottori University, Tottori, Tottori, Japan.
Abstract
Publisher
Ram Arti Publishers
Subject
General Engineering,General Business, Management and Accounting,General Mathematics,General Computer Science
Reference18 articles.
1. Anbalagan, P., & Vouk, M. (2008). On reliability analysis of open source software-fedora. In 2008 19th International Symposium on Software Reliability Engineering (ISSRE) (pp. 325-326). IEEE Computer Society.
2. Gopal, M.K., Govindaraj, M., Chandra, P., Shetty, P., & Raj, S. (2022). Bugtrac–a new improved bug tracking system. In 2022 IEEE Delhi Section Conference (DELCON) (pp. 1-7). IEEE. New Delhi, India.
3. Kaur, A., & Jindal, S.G. (2017). Bug report collection system (BRCS). In 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence (pp. 697-701). IEEE. Noida, India.
4. Lee, W., Jung, B.G., & Baik, J. (2008). Early reliability prediction: An approach to software reliability assessment in open software adoption stage. In 2008 Second International Conference on Secure System Integration and Reliability Improvement (pp. 226-227). IEEE. Yokohama, Japan.
5. Li, P.L., Herbsleb, J., & Shaw, M. (2005). Finding predictors of field defects for open source software systems in commonly available data sources: A case study of openbsd. In 11th IEEE International Software Metrics Symposium (METRICS'05) (pp. 10-pp). IEEE. Como, Italy.
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