Enhancing Large-Scale Code Understanding Through Goal Structuring Notation and Large Language Models

Abstract

Large language models (LLMs) aid programmers in understanding code but are limited by input length when handling large codebases. To address this, we propose using Goal Structuring Notation (GSN) – originally developed for articulating assurance cases in complex engineering projects – to represent and break down large codebases. We introduce a tool that leverages LLMs to automatically convert large code into GSN. The generated GSN provides an overview that simplifies code comprehension and enhances communication among programmers. Experimental results demonstrate that our approach significantly increases programmers’ confidence levels and reduces task completion times.