TTAFPred: Prediction of time to aging failure for software systems based on a two-stream multi-scale features fusion network-Reference-Cited by-同舟云学术

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TTAFPred: Prediction of time to aging failure for software systems based on a two-stream multi-scale features fusion network

Published:2024-07-22 Issue: Volume: Page:
ISSN:0963-9314
Container-title:Software Quality Journal
language:en
Short-container-title:Software Qual J

Author:

Jia Kai,Yu Xiao,Zhang Chen,Xie Wenzhi,Zhao Dongdong,Xiang Jianwen

Funder

Natural Science Foundation of Chongqing

National Key Research and Development Program of China

National Natural Science Foundation of China

Key Research and Development Program of Hubei Province

Key Research and Development Program of Hainan Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11219-024-09692-2.pdf

Reference55 articles.

1. Alonso, J., Matias, R., Vicente, E., Maria, A., & Trivedi, K. (2013). A comparative experimental study of software rejuvenation overhead. Performance Evaluation, 70(3), 231–250.

2. Andrade, E., Pietrantuono, R., Machida, F., & Cotroneo, D. (2023). A comparative analysis of software aging in image classifiers on cloud and edge. IEEE Transactions on Dependable and Secure Computing, 20(1), 563–573.

3. Bai, J., Chang, X., Trivedi, K. S., & Han, Z. (2021). Resilience-driven quantitative analysis of vehicle platooning service. IEEE Transactions on Vehicular Technology, 70(6), 5378–5389.

4. Bai, J., Chang, X., Machida, F., Jiang, L., Han, Z., & Trivedi, K. S. (2023). Impact of service function aging on the dependability for mec service function chain. IEEE Transactions on Dependable and Secure Computing, 20(4), 2811–2824.

5. Battisti, F., Silva, A., Pereira, L., Carvalho, T., Araujo, J., Choi, E., Nguyen, T. A., & Min, D. (2022). hLSTM-Aging: A hybrid LSTM model for software aging forecast. Applied Sciences, 12(13), 6412.

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