Abstract
Modern React-based web applications face significant performance challenges as application complexity increases, resulting in prolonged component loading times and degraded user experience. Search engines emphasis on Core Web Vitals metrics further necessitates performance optimization for maintaining web visibility. This paper presents ReactSmart, a machine learning framework that implements adaptive resource management for React component loading through predictive user behavior analysis. ReactSmart employs real-time decision making algorithms that analyze four contextual dimensions: user interaction patterns, application state, network conditions, and device capabilities. The system utilizes these inputs to proactively load components based on predicted user navigation paths, thereby reducing wait times and improving application responsiveness. Our experimental evaluation compares ReactSmart against React Suspense and Guess.js using 10,000 simulated user sessions across multiple application archetypes. Results demonstrate that ReactSmart achieves a 47.3% reduction in initial loading times, 52.1% improvement in Time-to-Interactive (TTI) metrics, and maintains 89.7% prediction accuracy for component usage forecasting within 30-second intervals. The machine learning model incorporates supervised learning techniques trained on user interaction datasets to optimize component prefetching decisions. This research contributes to bridging the gap between theoretical machine learning approaches and practical web performance engineering by providing an open-source framework that enables developers to implement intelligent component loading strategies without requiring deep machine learning expertise. The ReactSmart software and evaluation framework are freely available as open source at https://github.com/sameermankotia/ReactSmart-performance-analysis/tree/master