@inproceedings{kuhar-etal-2025-libevolutioneval,
title = "{L}ib{E}volution{E}val: A Benchmark and Study for Version-Specific Code Generation",
author = "Kuhar, Sachit and
Ahmad, Wasi Uddin and
Wang, Zijian and
Jain, Nihal and
Qian, Haifeng and
Ray, Baishakhi and
Ramanathan, Murali Krishna and
Ma, Xiaofei and
Deoras, Anoop",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.348/",
doi = "10.18653/v1/2025.naacl-long.348",
pages = "6826--6840",
ISBN = "979-8-89176-189-6",
abstract = "Recent advancements in code completion models have primarily focused on local file contexts. However, these studies do not fully capture the complexity of real-world software development, which often requires the use of rapidly-evolving public libraries. To address this gap, we introduce LibEvolutionEval, a comprehensive study that emphasizes the need to understand library evolution to perform accurate in-line code completions. LibEvolutionEvaloffers a version-specific code-completion task across eight libraries as they evolve over the years, along with an in-depth analysis of the evolution of two widely used and well-maintained public libraries: PyTorch and Matplotlib. We evaluate several popular models and find that public library evolution significantly affects their performance. To mitigate this, we explored how retrieving version-specific library documentation and prompt-based techniques can enhance model capability in dealing with these fast-evolving packages. This suggests a promising path forward for better handling fast-evolving libraries. Our tasks will be made publicly available upon acceptance."
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<abstract>Recent advancements in code completion models have primarily focused on local file contexts. However, these studies do not fully capture the complexity of real-world software development, which often requires the use of rapidly-evolving public libraries. To address this gap, we introduce LibEvolutionEval, a comprehensive study that emphasizes the need to understand library evolution to perform accurate in-line code completions. LibEvolutionEvaloffers a version-specific code-completion task across eight libraries as they evolve over the years, along with an in-depth analysis of the evolution of two widely used and well-maintained public libraries: PyTorch and Matplotlib. We evaluate several popular models and find that public library evolution significantly affects their performance. To mitigate this, we explored how retrieving version-specific library documentation and prompt-based techniques can enhance model capability in dealing with these fast-evolving packages. This suggests a promising path forward for better handling fast-evolving libraries. Our tasks will be made publicly available upon acceptance.</abstract>
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%0 Conference Proceedings
%T LibEvolutionEval: A Benchmark and Study for Version-Specific Code Generation
%A Kuhar, Sachit
%A Ahmad, Wasi Uddin
%A Wang, Zijian
%A Jain, Nihal
%A Qian, Haifeng
%A Ray, Baishakhi
%A Ramanathan, Murali Krishna
%A Ma, Xiaofei
%A Deoras, Anoop
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F kuhar-etal-2025-libevolutioneval
%X Recent advancements in code completion models have primarily focused on local file contexts. However, these studies do not fully capture the complexity of real-world software development, which often requires the use of rapidly-evolving public libraries. To address this gap, we introduce LibEvolutionEval, a comprehensive study that emphasizes the need to understand library evolution to perform accurate in-line code completions. LibEvolutionEvaloffers a version-specific code-completion task across eight libraries as they evolve over the years, along with an in-depth analysis of the evolution of two widely used and well-maintained public libraries: PyTorch and Matplotlib. We evaluate several popular models and find that public library evolution significantly affects their performance. To mitigate this, we explored how retrieving version-specific library documentation and prompt-based techniques can enhance model capability in dealing with these fast-evolving packages. This suggests a promising path forward for better handling fast-evolving libraries. Our tasks will be made publicly available upon acceptance.
%R 10.18653/v1/2025.naacl-long.348
%U https://aclanthology.org/2025.naacl-long.348/
%U https://doi.org/10.18653/v1/2025.naacl-long.348
%P 6826-6840
Markdown (Informal)
[LibEvolutionEval: A Benchmark and Study for Version-Specific Code Generation](https://aclanthology.org/2025.naacl-long.348/) (Kuhar et al., NAACL 2025)
ACL
- Sachit Kuhar, Wasi Uddin Ahmad, Zijian Wang, Nihal Jain, Haifeng Qian, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, and Anoop Deoras. 2025. LibEvolutionEval: A Benchmark and Study for Version-Specific Code Generation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 6826–6840, Albuquerque, New Mexico. Association for Computational Linguistics.