# Knowledge Graph Based Repository-Level Code Generation

**Authors:** Mihir Athale, Vishal Vaddina  
**arXiv:** [2505.14394](https://arxiv.org/abs/2505.14394)  
**Date:** 2025-05-20  
**Summary page:** https://www.summarizepaper.com/en/paper/2505.14394v1/

## Key points

- Authors Mihir Athale and Vishal Vaddina present a study on "Knowledge Graph Based Repository-Level Code Generation" focusing on Large Language Models (LLMs) and their impact on code generation.
- LLMs revolutionize code generation from natural language queries but struggle with contextual accuracy in dynamic codebases.
- Existing code search methods lack robustness, leading to subpar outcomes in code generation.
- The authors propose a knowledge graph-based approach to enhance code search and retrieval for better quality generated code in repository-level tasks.
- This approach represents code repositories as graphs to capture structural and relational information crucial for context-aware code generation.
- The framework uses a hybrid methodology for code retrieval to improve contextual relevance, monitor dependencies, generate resilient snippets, and maintain consistency with the existing codebase.
- Testing on the EvoCodeBench dataset shows that the knowledge graph-based method outperforms baseline approaches in quality and contextual accuracy significantly.
- This research highlights the potential of knowledge graph-based strategies in advancing coding assistance tools for improved outcomes within software repositories.

## Summary

Authors Mihir Athale and Vishal Vaddina present a groundbreaking study titled "Knowledge Graph Based Repository-Level Code Generation," which delves into the realm of Large Language Models (LLMs) and their impact on code generation. The authors highlight that while LLMs have revolutionized the process of generating code from natural language queries, they often face challenges in maintaining contextual accuracy, especially within dynamic codebases. Existing code search and retrieval methods are criticized for their lack of robustness in delivering high-quality and contextually relevant results, ultimately leading to subpar code generation outcomes. To address these limitations, the authors propose a novel knowledge graph-based approach aimed at enhancing code search and retrieval processes to elevate the quality of generated code within repository-level tasks. By representing code repositories as graphs, this approach captures crucial structural and relational information essential for fostering context-aware code generation. The framework leverages a hybrid methodology for code retrieval, designed to enhance contextual relevance, monitor inter-file modular dependencies, generate more resilient code snippets, and maintain consistency with the existing codebase. The efficacy of this proposed approach is rigorously tested using the Evolutionary Code Benchmark (EvoCodeBench) dataset—a benchmark specifically tailored for evaluating repository-level code generation techniques. Through comprehensive benchmarking exercises, the authors demonstrate that their knowledge graph-based method significantly outperforms baseline approaches in terms of both quality and contextual accuracy. These promising findings underscore the potential of knowledge graph-based strategies in advancing robust and context-sensitive coding assistance tools. In conclusion, Athale and Vaddina's research sheds light on the transformative capabilities of knowledge graph-based approaches in improving code search and retrieval mechanisms for enhanced code generation outcomes within evolving software repositories. This study not only contributes valuable insights to the field of computational linguistics but also paves the way for future advancements in context-aware coding assistance technologies.