Code Optimization Using Machine Learning Algorithms

Key words: reinforcement learning, Q-learning, performance, resource-intensive applications, Python, clustering, automation, performance improvement, Python libraries This article explores modern methods for code optimization using machine learning (ML) algorithms, with particular attention to reinforcement learning (RL) techniques. As software systems grow increasingly complex, RL algorithms such as Q-learning and Deep Q-learning provide significant advantages for enhancing application performance through the automation of the optimization process and adaptation to specific runtime conditions. The focus is placed on key optimization metrics, including time and space complexity, code modularity, and function call frequency, which are especially relevant for resource-intensive and high-performance applications. A comprehensive design approach is proposed, incorporating data collection, feature extraction, and code classification through clustering techniques, establishing a solid foundation for RL-based optimization strategies. Using Python and its libraries, such as NumPy and Matplotlib, RL algorithms are implemented and tested within a controlled environment, enabling an evaluation of their practical efficacy. Experimental results demonstrate reduced resource consumption and improved execution time, affirming the potential of RL for code enhancement. Furthermore, the article discusses prospects for applying RL to code optimization across various fields, opening avenues for future research in hybrid approaches that integrate traditional optimization techniques with ML and RL.