Code Graph Model (CGM): A Graph-Integrated Large Language Model for
  Repository-Level Software Engineering Tasks
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%.
68369babaffae1c74f432a1e
Hongyuan Tao
68369babaffae1c74f432a1f
Ying Zhang
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2025-05-28T19:14:20.825Z
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Ying ZHANG
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Zhenhao Tang
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2025-05-28T12:39:47.865Z
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Zhenhao Tang
user
Darian616
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Hongen Peng
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Xukun Zhu
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Bingchang Liu
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Yingguang Yang
admin_assigned
2025-05-28T12:40:15.278Z
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yingguang
user
yangyingguang
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Ziyin Zhang
claimed_verified
2025-05-28T08:56:43.238Z
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Ziyin Zhang
user
Geralt-Targaryen
68369babaffae1c74f432a26
Zhaogui Xu
68369babaffae1c74f432a27
Haipeng Zhang
68369babaffae1c74f432a28
Linchao Zhu
admin_assigned
2025-05-28T12:40:42.330Z
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Linchao Zhu
user
ffmpbgrnn
68369babaffae1c74f432a29
Rui Wang
68369babaffae1c74f432a2a
Hang Yu
claimed_verified
2025-05-28T19:14:18.595Z
6837243353739ee0d588d04c
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/CYU8xxEHxxGkwNVCpua6X.png
Hang Yu
user
fhlyhv
68369babaffae1c74f432a2b
Jianguo Li
68369babaffae1c74f432a2c
Peng Di
Code Graph Model (CGM): A Graph-Integrated Large Language Model for
  Repository-Level Software Engineering Tasks
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%.
Open-source Code Graph Models enhance repository-level code generation tasks by integrating code graph structures into LLMs' attention mechanisms, achieving high performance without agent-based approaches.
Large Language Models LLMs function-level code generation repository-level software engineering Code Graph Models CGMs attention mechanism SWE-bench Lite benchmark Qwen2.5-72B model graph RAG framework