EWDLL: Software Aging State Identification based on LightGBM-LR Hybrid Model

Abstract

Android systems are prone to software aging due to the accumulation of numerical errors and storage-related bugs during long-term operation, resulting in gradual performance degradation and sudden system hang-ups. Thus, it is very critical to accurately identify the aging state for improving the running reliability of Android systems. In this paper, we propose a novel software aging state identification method, named EWDLL. It first introduces the exponential Weibull distribution to simulate the aging state transfer process of the Android system, then it uses Fuzzy Analytical Hierarchy Process (FAHP) to weight the model parameters and resource utilization parameters. Finally, the weighted dataset is fed into the LightGBM-LR model to identify the software state. The experimental results show that our EWDLL method performs better in identifying the software aging state for Android system, i.e., it is 0.86% to 1.09% higher in identification accuracy than the pure LightGBM-LR model, about 10.00% and 4.54% to 4.95% higher than the traditional models KNN and RF, and 1.97% to 3.09% higher than single LightGBM model. Compared with the LR model, it has a maximum accuracy improvement of about 33.29% to 35.64%.