AI CODE EXPLANATION TOOL

Abstract

Software development increasingly requires efficient tools that help programmers understand unfamiliar source code, improve learning, and accelerate debugging. Recent advances in Artificial Intelligence (AI) and Large Language Models (LLMs) have enabled automated code interpretation by generating accurate and human-readable explanations. However, traditional documentation and manual code analysis are often incomplete, inconsistent, and timeconsuming, making software comprehension difficult for beginners and experienced developers alike. The proposed system utilizes user-submitted source code written in multiple programming languages collected through a web-based interface, where each input is analyzed to extract programming constructs, functions, variables, control flow, and logical relationships for explanation generation. During preprocessing, the submitted code is validated, cleaned, tokenized, languageidentified, and structurally parsed before being converted into semantic representations for efficient analysis and retrieval. The implementation integrates the Llama 3 Large Language Model through the Groq API, supported by Natural Language Processing techniques, semantic retrieval using Pinecone Vector Database, PostgreSQL for explanation history management, and a React.js–Node.js full-stack architecture for seamless interaction. System performance is evaluated using response latency, explanation relevance, semantic similarity, retrieval accuracy, and user satisfaction metrics. Experimental evaluation demonstrates that the Llama 3–Groq framework produces highly accurate, context-aware, and comprehensive code explanations with low response time and improved semantic understanding. The proposed approach significantly enhances code comprehension, learning efficiency, debugging support, and overall developer productivity through intelligent AI-assisted code explanation