International Journal of Advanced Research in Computer and Communication Engineering

Abstract: In the contemporary software engineering landscape, developers and students frequently encounter the challenge of onboarding to large, unfamiliar codebases. Platforms like GitHub host millions of repositories, yet understanding the underlying logic, architecture, and dependency flow of these projects remains a labor-intensive process dependent on manual traversal and often outdated documentation. To mitigate this inefficiency, this paper presents the Agentic AutoCode Analyzer, a web-based intelligent system designed to automate the comprehension of software repositories.
The proposed system accepts a GitHub repository URL, autonomously performs a shallow clone operation to minimize bandwidth usage, and recursively maps the directory structure to build a comprehensive context object. By integrating a Large Language Model (LLM) reasoning engine via a local or API-based inference layer, the system functions as an interactive "agent." This agent assists users by answering architectural queries, explaining specific code syntax, and summarizing project objectives. Experimental results indicate that the system significantly reduces the cognitive load required for code comprehension and offers a viable tool for both educational and professional software development environments.

Keywords: Artificial Intelligence, Static Code Analysis, Large Language Models (LLM), Software Engineering Education, Automated Documentation, React.js, Node.js.

Downloads: | DOI: 10.17148/IJARCCE.2026.15197