Uncovering Code Insights: Leveraging GitHub Artifacts for Deeper Code Understanding

TL;DR

This paper presents a system that leverages GitHub artifacts like pull requests, issues, and commit messages to improve large language models' understanding of code, providing more grounded and helpful explanations.

Contribution

It introduces a novel method combining GitHub context extraction with LLMs to generate and validate code explanations, enhancing interpretability in software development.

Findings

Generated explanations are often helpful and non-trivial.

System reduces hallucinations in code explanations.

User study indicates improved understanding with insights.

Abstract

Understanding the purpose of source code is a critical task in software maintenance, onboarding, and modernization. While large language models (LLMs) have shown promise in generating code explanations, they often lack grounding in the broader software engineering context. We propose a novel approach that leverages natural language artifacts from GitHub -- such as pull request descriptions, issue descriptions and discussions, and commit messages -- to enhance LLM-based code understanding. Our system consists of three components: one that extracts and structures relevant GitHub context, another that uses this context to generate high-level explanations of the code's purpose, and a third that validates the explanation. We implemented this as a standalone tool, as well as a server within the Model Context Protocol (MCP), enabling integration with other AI-assisted development tools. Our main use case is that of enhancing a standard LLM-based code explanation with code insights that our system generates. To evaluate explanations' quality, we conducted a small scale user study, with developers of several open projects, as well as developers of proprietary projects. Our user study indicates that when insights are generated they often are helpful and non trivial, and are free from hallucinations.