@inproceedings{sedykh-etal-2024-searching,
title = "Searching by Code: A New {S}earch{B}y{S}nippet Dataset and {S}nippe{R} Retrieval Model for Searching by Code Snippets",
author = "Sedykh, Ivan and
Sorokin, Nikita and
Abulkhanov, Dmitry and
Nikolenko, Sergey I. and
Malykh, Valentin",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1261/",
pages = "14472--14477",
abstract = "Code search is an important and well-studied task, but it usually means searching for code by a text query. We argue that using a code snippet (and possibly an error traceback) as a query while looking for bugfixing instructions and code samples is a natural use case not covered by prior art. Moreover, existing datasets use code comments rather than full-text descriptions as text, making them unsuitable for this use case. We present a new SearchBySnippet dataset implementing the search-by-code use case based on StackOverflow data; we show that on SearchBySnippet, existing architectures fall short of a simple BM25 baseline even after fine-tuning. We present a new single encoder model SnippeR that outperforms several strong baselines on SearchBySnippet with a result of 0.451 Recall@10; we propose the SearchBySnippet dataset and SnippeR as a new important benchmark for code search evaluation."
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%0 Conference Proceedings
%T Searching by Code: A New SearchBySnippet Dataset and SnippeR Retrieval Model for Searching by Code Snippets
%A Sedykh, Ivan
%A Sorokin, Nikita
%A Abulkhanov, Dmitry
%A Nikolenko, Sergey I.
%A Malykh, Valentin
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F sedykh-etal-2024-searching
%X Code search is an important and well-studied task, but it usually means searching for code by a text query. We argue that using a code snippet (and possibly an error traceback) as a query while looking for bugfixing instructions and code samples is a natural use case not covered by prior art. Moreover, existing datasets use code comments rather than full-text descriptions as text, making them unsuitable for this use case. We present a new SearchBySnippet dataset implementing the search-by-code use case based on StackOverflow data; we show that on SearchBySnippet, existing architectures fall short of a simple BM25 baseline even after fine-tuning. We present a new single encoder model SnippeR that outperforms several strong baselines on SearchBySnippet with a result of 0.451 Recall@10; we propose the SearchBySnippet dataset and SnippeR as a new important benchmark for code search evaluation.
%U https://aclanthology.org/2024.lrec-main.1261/
%P 14472-14477
Markdown (Informal)
[Searching by Code: A New SearchBySnippet Dataset and SnippeR Retrieval Model for Searching by Code Snippets](https://aclanthology.org/2024.lrec-main.1261/) (Sedykh et al., LREC-COLING 2024)
ACL