@inproceedings{yang-etal-2025-coast,
title = "{COAST}: Enhancing the Code Debugging Ability of {LLM}s through Communicative Agent Based Data Synthesis",
author = "Yang, Weiqing and
Wang, Hanbin and
Liu, Zhenghao and
Li, Xinze and
Yan, Yukun and
Wang, Shuo and
Gu, Yu and
Yu, Minghe and
Liu, Zhiyuan and
Yu, Ge",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.139/",
doi = "10.18653/v1/2025.findings-naacl.139",
pages = "2570--2585",
ISBN = "979-8-89176-195-7",
abstract = "Code debugging is a vital stage of software development, essential for ensuring the reliability and performance of Large Language Models (LLMs) in the code generation task. Human debugging typically follows a multi-stage process, which includes Bug Localization, Bug Identification, Code Repair, and Code Recognition. However, existing code debugging benchmarks predominantly focus on the Code Repair stage, which offers only a limited perspective on evaluating the debugging capabilities of LLMs. In this paper, we introduce DEBUGEVAL, a comprehensive benchmark for evaluating the debugging abilities of LLMs by emulating the multi-stage human debugging process. Through evaluating on DEBUGEVAL, we observe that 7B-scale models consistently underperform compared to their larger counterparts, highlighting their limitations in comprehending code semantics. In this case, we propose the COmmunicative Agent-based data SynThesis (COAST) framework, which employs a multi-agent system to generate high-quality training data for supervised fine-tuning (SFT). Experimental results demonstrate that COAST-generated data outperform human-curated and GPT-4-generated data, enabling 7B-scale LLMs to achieve debugging performance comparable to GPT-3.5. All data and codes are available at https://github.com/NEUIR/COAST."
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<abstract>Code debugging is a vital stage of software development, essential for ensuring the reliability and performance of Large Language Models (LLMs) in the code generation task. Human debugging typically follows a multi-stage process, which includes Bug Localization, Bug Identification, Code Repair, and Code Recognition. However, existing code debugging benchmarks predominantly focus on the Code Repair stage, which offers only a limited perspective on evaluating the debugging capabilities of LLMs. In this paper, we introduce DEBUGEVAL, a comprehensive benchmark for evaluating the debugging abilities of LLMs by emulating the multi-stage human debugging process. Through evaluating on DEBUGEVAL, we observe that 7B-scale models consistently underperform compared to their larger counterparts, highlighting their limitations in comprehending code semantics. In this case, we propose the COmmunicative Agent-based data SynThesis (COAST) framework, which employs a multi-agent system to generate high-quality training data for supervised fine-tuning (SFT). Experimental results demonstrate that COAST-generated data outperform human-curated and GPT-4-generated data, enabling 7B-scale LLMs to achieve debugging performance comparable to GPT-3.5. All data and codes are available at https://github.com/NEUIR/COAST.</abstract>
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%0 Conference Proceedings
%T COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis
%A Yang, Weiqing
%A Wang, Hanbin
%A Liu, Zhenghao
%A Li, Xinze
%A Yan, Yukun
%A Wang, Shuo
%A Gu, Yu
%A Yu, Minghe
%A Liu, Zhiyuan
%A Yu, Ge
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F yang-etal-2025-coast
%X Code debugging is a vital stage of software development, essential for ensuring the reliability and performance of Large Language Models (LLMs) in the code generation task. Human debugging typically follows a multi-stage process, which includes Bug Localization, Bug Identification, Code Repair, and Code Recognition. However, existing code debugging benchmarks predominantly focus on the Code Repair stage, which offers only a limited perspective on evaluating the debugging capabilities of LLMs. In this paper, we introduce DEBUGEVAL, a comprehensive benchmark for evaluating the debugging abilities of LLMs by emulating the multi-stage human debugging process. Through evaluating on DEBUGEVAL, we observe that 7B-scale models consistently underperform compared to their larger counterparts, highlighting their limitations in comprehending code semantics. In this case, we propose the COmmunicative Agent-based data SynThesis (COAST) framework, which employs a multi-agent system to generate high-quality training data for supervised fine-tuning (SFT). Experimental results demonstrate that COAST-generated data outperform human-curated and GPT-4-generated data, enabling 7B-scale LLMs to achieve debugging performance comparable to GPT-3.5. All data and codes are available at https://github.com/NEUIR/COAST.
%R 10.18653/v1/2025.findings-naacl.139
%U https://aclanthology.org/2025.findings-naacl.139/
%U https://doi.org/10.18653/v1/2025.findings-naacl.139
%P 2570-2585
Markdown (Informal)
[COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis](https://aclanthology.org/2025.findings-naacl.139/) (Yang et al., Findings 2025)
ACL
- Weiqing Yang, Hanbin Wang, Zhenghao Liu, Xinze Li, Yukun Yan, Shuo Wang, Yu Gu, Minghe Yu, Zhiyuan Liu, and Ge Yu. 2025. COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 2570–2585, Albuquerque, New Mexico. Association for Computational Linguistics.