Intelligent Angular Architecture: Machine Learning-Based Component Recommendation Systems for Enterprise-Scale Development

The increasing complexity of enterprise-scale Angular applications necessitates intelligent mechanisms to improve component reuse, maintain architectural consistency, and enhance developer productivity. The conventional methods of development frequently depend on the process of decision making that is carried out by hand and, therefore, may result in overlapping components, irregular design patterns, and unproductive work processes. To overcome these challenges, this paper suggests a smart Angular design, which will incorporate a machine learning-based component recommendation system. The system uses historical source code repositories, component usage log, and contextual development data to create predictive model that is up to date and can offer optimal components in real-time. The suggested framework includes the use of cutting-edge methods, including feature engineering, similarity analysis, and code embedding’s offered by natural language processing to clear structural and semantic relations among components. It easily fits with the Angular CLI and development environments and offers the context-sensitive suggestions without interfering with the current workflows. The experime