Abstract
The Android communication system is constantly evolving and is often influenced by multiple factors such as network fluctuations, resource competition, module coupling, and version changes. This can lead to increased message latency, unstable connection maintenance, and abnormal spread caused by local updates. Based on these operational characteristics, a multi-source feature set covering device resources, system behavior, communication links, and version evolution information is constructed. Machine learning is used to identify performance change trends and abnormal triggering risks, and the prediction results are embedded in evolution processes such as gray release, change assessment, abnormal rollback, and online correction. Experimental comparisons show that the prediction model has a high degree of compatibility in identifying connection anomalies, warning of latency fluctuations, and intercepting release risks. After collaborative evolution control, the system stability, update controllability, and fault response efficiency have all been improved. This research provides a feasible technical reference for the high-reliability evolution of Android communication software.
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How to cite this paper
Research on Machine Learning-based Performance Prediction and High-reliability Software Evolution Mechanisms for Android Communication Systems
How to cite this paper: Junhao Su. (2026) Research on Machine Learning-based Performance Prediction and High-reliability Software Evolution Mechanisms for Android Communication Systems. Advances in Computer and Communication, 7(3), 122-125.
DOI: http://dx.doi.org/10.26855/acc.2026.09.001