@inproceedings{li-etal-2025-benchmarking,
title = "Benchmarking Long-Context Language Models on Long Code Understanding",
author = "Li, Jia and
Guo, Xuyuan and
Li, Lei and
Zhang, Kechi and
Li, Ge and
Li, Jia and
Tao, Zhengwei and
Liu, Fang and
Tao, Chongyang and
Zhu, Yuqi and
Jin, Zhi",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1324/",
doi = "10.18653/v1/2025.acl-long.1324",
pages = "27309--27327",
ISBN = "979-8-89176-251-0",
abstract = "Current advanced long-context language models offer great potential for real-world software engineering applications. However, progress in this critical domain remains hampered by a fundamental limitation: the absence of a rigorous evaluation framework for long code understanding. To gap this obstacle, we propose a long code understanding benchmark LongCodeU from four aspects (8 tasks) to evaluate LCLMs' long code understanding ability required for practical applications, including code unit perception, intra-code unit understanding, inter-code unit relation understanding, and long code documentation understanding. We evaluate 9 popular LCLMs on LongCodeU (i.e., 6 general models and 3 code models). Our experimental results reveal key limitations in current LCLMs' capabilities for long code understanding. Particularly, the performance of LCLMs drops dramatically when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows. In the four aspects, inter-code unit relation understanding is the most challenging for LCLMs. Our study provides valuable insights for optimizing LCLMs and driving advancements in software engineering."
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<abstract>Current advanced long-context language models offer great potential for real-world software engineering applications. However, progress in this critical domain remains hampered by a fundamental limitation: the absence of a rigorous evaluation framework for long code understanding. To gap this obstacle, we propose a long code understanding benchmark LongCodeU from four aspects (8 tasks) to evaluate LCLMs’ long code understanding ability required for practical applications, including code unit perception, intra-code unit understanding, inter-code unit relation understanding, and long code documentation understanding. We evaluate 9 popular LCLMs on LongCodeU (i.e., 6 general models and 3 code models). Our experimental results reveal key limitations in current LCLMs’ capabilities for long code understanding. Particularly, the performance of LCLMs drops dramatically when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows. In the four aspects, inter-code unit relation understanding is the most challenging for LCLMs. Our study provides valuable insights for optimizing LCLMs and driving advancements in software engineering.</abstract>
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%0 Conference Proceedings
%T Benchmarking Long-Context Language Models on Long Code Understanding
%A Li, Jia
%A Guo, Xuyuan
%A Li, Lei
%A Zhang, Kechi
%A Li, Ge
%A Tao, Zhengwei
%A Liu, Fang
%A Tao, Chongyang
%A Zhu, Yuqi
%A Jin, Zhi
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F li-etal-2025-benchmarking
%X Current advanced long-context language models offer great potential for real-world software engineering applications. However, progress in this critical domain remains hampered by a fundamental limitation: the absence of a rigorous evaluation framework for long code understanding. To gap this obstacle, we propose a long code understanding benchmark LongCodeU from four aspects (8 tasks) to evaluate LCLMs’ long code understanding ability required for practical applications, including code unit perception, intra-code unit understanding, inter-code unit relation understanding, and long code documentation understanding. We evaluate 9 popular LCLMs on LongCodeU (i.e., 6 general models and 3 code models). Our experimental results reveal key limitations in current LCLMs’ capabilities for long code understanding. Particularly, the performance of LCLMs drops dramatically when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows. In the four aspects, inter-code unit relation understanding is the most challenging for LCLMs. Our study provides valuable insights for optimizing LCLMs and driving advancements in software engineering.
%R 10.18653/v1/2025.acl-long.1324
%U https://aclanthology.org/2025.acl-long.1324/
%U https://doi.org/10.18653/v1/2025.acl-long.1324
%P 27309-27327
Markdown (Informal)
[Benchmarking Long-Context Language Models on Long Code Understanding](https://aclanthology.org/2025.acl-long.1324/) (Li et al., ACL 2025)
ACL
- Jia Li, Xuyuan Guo, Lei Li, Kechi Zhang, Ge Li, Jia Li, Zhengwei Tao, Fang Liu, Chongyang Tao, Yuqi Zhu, and Zhi Jin. 2025. Benchmarking Long-Context Language Models on Long Code Understanding. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 27309–27327, Vienna, Austria. Association for Computational Linguistics.