@inproceedings{zuchen-etal-2025-oasis,
title = "{OASIS}: Order-Augmented Strategy for Improved Code Search",
author = "Gao, Zuchen and
Zhan, Zizheng and
Li, Xianming and
Yu, Erxin and
Zhang, Haotian and
Chen, Bin and
Zhang, Yuqun and
Li, Jing",
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.904/",
doi = "10.18653/v1/2025.acl-long.904",
pages = "18451--18467",
ISBN = "979-8-89176-251-0",
abstract = "Code embeddings capture the semantic representations of code and are crucial for various code-related large language model (LLM) applications, such as code search. Previous training primarily relies on optimizing the InfoNCE loss by comparing positive natural language (NL)-code pairs with in-batch negatives. However, due to the sparse nature of code contexts, training solely by comparing the major differences between positive and negative pairs may fail to capture deeper semantic nuances. To address this issue, we propose a novel order-augmented strategy for improved code search (OASIS). It leverages order-based similarity labels to train models to capture subtle differences in similarity among negative pairs. Extensive benchmark evaluations demonstrate that our OASIS model significantly outperforms previous state-of-the-art models focusing solely on major positive-negative differences. It underscores the value of exploiting subtle differences among negative pairs with order labels for effective code embedding training."
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<abstract>Code embeddings capture the semantic representations of code and are crucial for various code-related large language model (LLM) applications, such as code search. Previous training primarily relies on optimizing the InfoNCE loss by comparing positive natural language (NL)-code pairs with in-batch negatives. However, due to the sparse nature of code contexts, training solely by comparing the major differences between positive and negative pairs may fail to capture deeper semantic nuances. To address this issue, we propose a novel order-augmented strategy for improved code search (OASIS). It leverages order-based similarity labels to train models to capture subtle differences in similarity among negative pairs. Extensive benchmark evaluations demonstrate that our OASIS model significantly outperforms previous state-of-the-art models focusing solely on major positive-negative differences. It underscores the value of exploiting subtle differences among negative pairs with order labels for effective code embedding training.</abstract>
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%0 Conference Proceedings
%T OASIS: Order-Augmented Strategy for Improved Code Search
%A Gao, Zuchen
%A Zhan, Zizheng
%A Li, Xianming
%A Yu, Erxin
%A Zhang, Haotian
%A Chen, Bin
%A Zhang, Yuqun
%A Li, Jing
%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 zuchen-etal-2025-oasis
%X Code embeddings capture the semantic representations of code and are crucial for various code-related large language model (LLM) applications, such as code search. Previous training primarily relies on optimizing the InfoNCE loss by comparing positive natural language (NL)-code pairs with in-batch negatives. However, due to the sparse nature of code contexts, training solely by comparing the major differences between positive and negative pairs may fail to capture deeper semantic nuances. To address this issue, we propose a novel order-augmented strategy for improved code search (OASIS). It leverages order-based similarity labels to train models to capture subtle differences in similarity among negative pairs. Extensive benchmark evaluations demonstrate that our OASIS model significantly outperforms previous state-of-the-art models focusing solely on major positive-negative differences. It underscores the value of exploiting subtle differences among negative pairs with order labels for effective code embedding training.
%R 10.18653/v1/2025.acl-long.904
%U https://aclanthology.org/2025.acl-long.904/
%U https://doi.org/10.18653/v1/2025.acl-long.904
%P 18451-18467
Markdown (Informal)
[OASIS: Order-Augmented Strategy for Improved Code Search](https://aclanthology.org/2025.acl-long.904/) (Gao et al., ACL 2025)
ACL
- Zuchen Gao, Zizheng Zhan, Xianming Li, Erxin Yu, Haotian Zhang, Bin Chen, Yuqun Zhang, and Jing Li. 2025. OASIS: Order-Augmented Strategy for Improved Code Search. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 18451–18467, Vienna, Austria. Association for Computational Linguistics.