A Literature Review on AI-Powered Smart Code Base Navigator

Abstract - In contemporary software development, the vast size of codebases poses challenges in locating, comprehending, and reusing code. Traditional search tools that rely on keywords often fall short in capturing a developer's true intent, thus impeding efficiency. This initiative introduces an AI-Powered Smart Code Base Navigator, a system designed to facilitate semantic code search, context-aware code completion, and streamlined navigation within extensive Python codebases. By utilizing pre-trained models such as CodeBERT and retrieval- augmented methods, the system can interpret natural language queries and deliver pertinent code. Additionally, it offers features like jump-to-definition and intelligent suggestions, boosting developer productivity. As a web- based tool, the navigator exemplifies how AI can effectively connect natural language with programming code, greatly enhancing software engineering methodologies. Key Words: AI, Code Navigation, Semantic Search, Retrieval- Augmented Generation, Large Language Models.