Software aging prediction framework based on optimized TCN model with grey correlation analysis

Yanchao Wang; Jiangyi Yao; Xiongwei Li; Linyun Liu

doi:10.1117/12.2656560

20 October 2022 Software aging prediction framework based on optimized TCN model with grey correlation analysis

Yanchao Wang, Jiangyi Yao, Xiongwei Li, Linyun Liu

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124512O (2022) https://doi.org/10.1117/12.2656560
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

The temporal convolutional network (TCN) model has the characteristics of strong parallelism and stable gradient in time series processing. The structural depth of the model is related to the input length, convolution kernel and dilation factor. In order to further improve the accuracy of prediction, this paper proposes a software aging prediction framework based on TCN model optimized by grey relational analysis. Collecting available memory data as the input of the framework, determine the length of the input nodes of the TCN model through gray correlation analysis, and then conduct training and prediction, and evaluate the efficiency of the model by checking the average error between the predicted output memory and the actual memory. Then change the length of the input chunk to carry out a comparative experiment, which verifies the effectiveness of the grey relational degree analysis.

Citation Download Citation

Yanchao Wang, Jiangyi Yao, Xiongwei Li, and Linyun Liu "Software aging prediction framework based on optimized TCN model with grey correlation analysis", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124512O (20 October 2022); https://doi.org/10.1117/12.2656560

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