Research On Software Performance Optimization Strategy Based on Machine Learning

Abstract

This paper discusses the core content and practical application of designing software performance optimization strategies based on machine learning. The paper also covers the collection and processing of performance data, as well as feature extraction and selection, which provide a solid foundation for training machine learning models. It emphasizes the importance of model selection and training, and outlines the key steps for constructing an optimal decision model. The practical application case demonstrates that a machine learning-based performance optimization strategy can effectively enhance the operational efficiency and stability of software systems, thereby improving the user experience. Additionally, the challenges of data quality and model stability that may arise in practical machine learning applications are also highlighted. In the future, as technology continues to advance, the potential applications of machine learning in software performance optimization will expand.