@inproceedings{huang-etal-2025-mldebugging,
title = "{MLD}ebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios",
author = "Huang, JinYang and
Feng, Xiachong and
Chen, Qiguang and
Zhao, Hanjie and
Cheng, Zihui and
Bai, Jiesong and
Zhou, Jingxuan and
Li, Min and
Qin, Libo",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.305/",
doi = "10.18653/v1/2025.findings-acl.305",
pages = "5866--5879",
ISBN = "979-8-89176-256-5",
abstract = "Code debugging is a crucial task in software engineering, which attracts increasing attention. While remarkable success has been made in the era of large language models (LLMs), current research still focuses on the simple no-library or single-library setting, ignoring the complex multi-library scenario in real-world applications. To address this limitation, we make the first attempt to introduce MLDebugging (Multi-Library Debugging), a comprehensive benchmark designed to assess debugging challenges within multi-library Python code. Specifically, MLDebugging encompasses 126 distinct Python libraries, covering a wide range of multi-library code issues, categorized into seven distinct types. Furthermore, we conduct a thorough evaluation of MLDebugging using both mainstream open-source and closed-source LLMs and highlight that current LLMs still struggle to correctly perform code debugging across multi-library scenarios. We hope this work can uncover the potential of LLMs in multi-library debugging scenario and offer insights for future research."
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<abstract>Code debugging is a crucial task in software engineering, which attracts increasing attention. While remarkable success has been made in the era of large language models (LLMs), current research still focuses on the simple no-library or single-library setting, ignoring the complex multi-library scenario in real-world applications. To address this limitation, we make the first attempt to introduce MLDebugging (Multi-Library Debugging), a comprehensive benchmark designed to assess debugging challenges within multi-library Python code. Specifically, MLDebugging encompasses 126 distinct Python libraries, covering a wide range of multi-library code issues, categorized into seven distinct types. Furthermore, we conduct a thorough evaluation of MLDebugging using both mainstream open-source and closed-source LLMs and highlight that current LLMs still struggle to correctly perform code debugging across multi-library scenarios. We hope this work can uncover the potential of LLMs in multi-library debugging scenario and offer insights for future research.</abstract>
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%0 Conference Proceedings
%T MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios
%A Huang, JinYang
%A Feng, Xiachong
%A Chen, Qiguang
%A Zhao, Hanjie
%A Cheng, Zihui
%A Bai, Jiesong
%A Zhou, Jingxuan
%A Li, Min
%A Qin, Libo
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F huang-etal-2025-mldebugging
%X Code debugging is a crucial task in software engineering, which attracts increasing attention. While remarkable success has been made in the era of large language models (LLMs), current research still focuses on the simple no-library or single-library setting, ignoring the complex multi-library scenario in real-world applications. To address this limitation, we make the first attempt to introduce MLDebugging (Multi-Library Debugging), a comprehensive benchmark designed to assess debugging challenges within multi-library Python code. Specifically, MLDebugging encompasses 126 distinct Python libraries, covering a wide range of multi-library code issues, categorized into seven distinct types. Furthermore, we conduct a thorough evaluation of MLDebugging using both mainstream open-source and closed-source LLMs and highlight that current LLMs still struggle to correctly perform code debugging across multi-library scenarios. We hope this work can uncover the potential of LLMs in multi-library debugging scenario and offer insights for future research.
%R 10.18653/v1/2025.findings-acl.305
%U https://aclanthology.org/2025.findings-acl.305/
%U https://doi.org/10.18653/v1/2025.findings-acl.305
%P 5866-5879
Markdown (Informal)
[MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios](https://aclanthology.org/2025.findings-acl.305/) (Huang et al., Findings 2025)
ACL
- JinYang Huang, Xiachong Feng, Qiguang Chen, Hanjie Zhao, Zihui Cheng, Jiesong Bai, Jingxuan Zhou, Min Li, and Libo Qin. 2025. MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios. In Findings of the Association for Computational Linguistics: ACL 2025, pages 5866–5879, Vienna, Austria. Association for Computational Linguistics.