Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks

• URL: http://arxiv.org/abs/2505.16901v4
• Date: Mon, 23 Jun 2025 20:05:49 GMT
• Title: Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks
• Authors: Hongyuan Tao, Ying Zhang, Zhenhao Tang, Hongen Peng, Xukun Zhu, Bingchang Liu, Yingguang Yang, Ziyin Zhang, Zhaogui Xu, Haipeng Zhang, Linchao Zhu, Rui Wang, Hang Yu, Jianguo Li, Peng Di,
• Abstract summary: Large Language Models (LLMs) have shown promise in function-level code generation, yet repository-level software engineering tasks remain challenging.<n>This paper investigates whether open-source LLMs can effectively address repository-level tasks without requiring agent-based approaches.<n>We introduce Code Graph Models (CGMs), which integrate repository code graph structures into the LLM's attention mechanism.
• Score: 42.79558714652442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in Large Language Models (LLMs) have shown promise in function-level code generation, yet repository-level software engineering tasks remain challenging. Current solutions predominantly rely on proprietary LLM agents, which introduce unpredictability and limit accessibility, raising concerns about data privacy and model customization. This paper investigates whether open-source LLMs can effectively address repository-level tasks without requiring agent-based approaches. We demonstrate this is possible by enabling LLMs to comprehend functions and files within codebases through their semantic information and structural dependencies. To this end, we introduce Code Graph Models (CGMs), which integrate repository code graph structures into the LLM's attention mechanism and map node attributes to the LLM's input space using a specialized adapter. When combined with an agentless graph RAG framework, our approach achieves a 43.00% resolution rate on the SWE-bench Lite benchmark using the open-source Qwen2.5-72B model. This performance ranks first among open weight models, second among methods with open-source systems, and eighth overall, surpassing the previous best open-source model-based method by 12.33%.

Related papers

• Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects [53.24831948221361]
  Graph-augmented LLM Agents (GLA) enhance structure, continuity, and coordination in complex agent systems.<n>This paper offers a timely and comprehensive overview of recent advances and highlights key directions for future work.<n>We hope this paper can serve as a roadmap for future research on GLA and foster a deeper understanding of the role of graphs in GLA agent systems.
  arXiv  Detail & Related papers  (2025-07-29T00:27:12Z)
• LLM-KG-Bench 3.0: A Compass for SemanticTechnology Capabilities in the Ocean of LLMs [0.12564343689544843]
  Current Large Language Models (LLMs) can assist developing program code beside many other things, but can they support working with Knowledge Graphs (KGs) as well?<n>The LLM-KG-Bench framework in Version 3.0 is designed to answer these questions.<n>It consists of a set of tasks for automated evaluation of LLM answers and covers different aspects of working with semantic technologies.
  arXiv  Detail & Related papers  (2025-05-19T13:29:27Z)
• Data-centric Federated Graph Learning with Large Language Models [34.224475952206404]
  In federated graph learning (FGL), a complete graph is divided into multiple subgraphs stored in each client due to privacy concerns.<n>A pain point of FGL is the heterogeneity problem, where nodes or structures present non-IID properties among clients.<n>We propose a general framework that innovatively decomposes the task of large language models for FGL into two sub-tasks theoretically.
  arXiv  Detail & Related papers  (2025-03-25T08:43:08Z)
• When Do LLMs Help With Node Classification? A Comprehensive Analysis [21.120619437937382]
  We develop a comprehensive and testbed for node classification using Large Language Models (LLMs)<n>It includes 10 homophilic datasets, 4 heterophilic datasets, 8 LLM-based algorithms, 8 classic baselines, and 3 learning paradigms.<n>Our findings uncover 8 insights, e.g., (1) LLM-based methods can significantly outperform traditional methods in a semi-supervised setting, while the advantage is marginal in a supervised setting.
  arXiv  Detail & Related papers  (2025-02-02T15:56:05Z)
• SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution [56.9361004704428]
  Large Language Models (LLMs) have demonstrated remarkable proficiency across a variety of complex tasks.<n>SWE-Fixer is a novel open-source framework designed to effectively and efficiently resolve GitHub issues.<n>We assess our approach on the SWE-Bench Lite and Verified benchmarks, achieving competitive performance among open-source models.
  arXiv  Detail & Related papers  (2025-01-09T07:54:24Z)
• OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models [76.59316249991657]
  Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems.<n>While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs remain limited.<n>We introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
  arXiv  Detail & Related papers  (2024-11-07T17:47:25Z)
• GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration [43.96008600046952]
  GraphTeam consists of five LLM-based agents from three modules, and the agents with different specialities can collaborate to address complex problems.<n>Experiments on six graph analysis benchmarks demonstrate that GraphTeam achieves state-of-the-art performance with an average 25.85% improvement over the best baseline in terms of accuracy.
  arXiv  Detail & Related papers  (2024-10-23T17:02:59Z)
• How to Make LLMs Strong Node Classifiers? [70.14063765424012]
  Language Models (LMs) are challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs)<n>We propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art (SOTA) GNNs on node classification tasks.
  arXiv  Detail & Related papers  (2024-10-03T08:27:54Z)
• Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models [88.4320775961431]
  We introduce ProGraph, a benchmark for large language models (LLMs) to process graphs.<n>Our findings reveal that the performance of current LLMs is unsatisfactory, with the best model achieving only 36% accuracy.<n>We propose LLM4Graph datasets, which include crawled documents and auto-generated codes based on 6 widely used graph libraries.
  arXiv  Detail & Related papers  (2024-09-29T11:38:45Z)
• Knowledge Fusion of Large Language Models [73.28202188100646]
  This paper introduces the notion of knowledge fusion for large language models (LLMs) We externalize their collective knowledge and unique strengths, thereby elevating the capabilities of the target model beyond those of any individual source LLM. Our findings confirm that the fusion of LLMs can improve the performance of the target model across a range of capabilities such as reasoning, commonsense, and code generation.
  arXiv  Detail & Related papers  (2024-01-19T05:02:46Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.