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ACM Softw. Eng."],"published-print":{"date-parts":[[2025,6,19]]},"abstract":"<jats:p>Code search is a crucial task in software engineering, aiming to retrieve code snippets that are semantically relevant to a natural language query. Recently, Pre-trained Language Models (PLMs) have shown remarkable success and are widely adopted for code search tasks. However, PLM-based methods often struggle in crossdomain scenarios. When applied to a new domain, they typically require extensive fine-tuning with substantial data. Even worse, the data scarcity problem in new domains often forces these methods to operate in a zero-shot setting, resulting in a significant decline in performance. RAPID, which generates synthetic data for model fine-tuning, is currently the only effective method for zero-shot cross-domain code search. Despite its effectiveness, RAPID demands substantial computational resources for fine-tuning and needs to maintain specialized models for each domain, underscoring the need for a zero-shot, fine-tuning-free approach for cross-domain code search.<\/jats:p>\n                  <jats:p>The key to tackling zero-shot cross-domain code search lies in bridging the gaps among domains. In this work, we propose to break the query-code matching process of code search into two simpler tasks: query-comment matching and code-code matching. We first conduct an empirical study to investigate the effectiveness of these two matching schemas in zero-shot cross-domain code search. Our findings highlight the strong complementarity among the three matching schemas, i.e., query-code, query-comment, and code-code matching. Based on the findings, we propose CodeBridge, a zero-shot, fine-tuning-free approach for crossdomain code search. Specifically, CodeBridge first employs zero-shot prompting to guide Large Language Models (LLMs) to generate a comment for each code snippet in the codebase and produce a code for each query. Subsequently, it encodes queries, code snippets, comments, and the generated code using PLMs and assesses similarities through three matching schemas: query-code, query-comment, and generated code-code. Lastly, CodeBridge leverages a sampling-based fusion approach that combines these three similarity scores to rank the final search outcomes. Experimental results show that our approach outperforms the state-of-the-art PLM-based code search approaches, i.e., CoCoSoDa and UniXcoder, by an average of 21.4% and 24.9% in MRR, respectively, across three datasets. Our approach also yields results that are better than or comparable to those of the zero-shot cross-domain code search approach RAPID, which requires costly fine-tuning.<\/jats:p>","DOI":"10.1145\/3729357","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T11:15:34Z","timestamp":1750331734000},"page":"1937-1959","source":"Crossref","is-referenced-by-count":5,"title":["Zero-Shot Cross-Domain Code Search without Fine-Tuning"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4613-247X","authenticated-orcid":false,"given":"Keyu","family":"Liang","sequence":"first","affiliation":[{"name":"Zhejiang University, The State Key Laboratory of Blockchain and Data Security, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1981-1626","authenticated-orcid":false,"given":"Zhongxin","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang University, The State Key Laboratory of Blockchain and Data Security, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8283-9146","authenticated-orcid":false,"given":"Chao","family":"Liu","sequence":"additional","affiliation":[{"name":"Chongqing University, School of Big Data and Software Engineering, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7657-6653","authenticated-orcid":false,"given":"Zhiyuan","family":"Wan","sequence":"additional","affiliation":[{"name":"Zhejiang University, The State Key Laboratory of Blockchain and Data Security, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4367-7201","authenticated-orcid":false,"given":"David","family":"Lo","sequence":"additional","affiliation":[{"name":"Singapore Management University, School of Computing and Information Systems, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4111-4189","authenticated-orcid":false,"given":"Xiaohu","family":"Yang","sequence":"additional","affiliation":[{"name":"Zhejiang University, The State Key Laboratory of Blockchain and Data Security, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,19]]},"reference":[{"key":"e_1_3_1_1_1","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et al.. 2023. 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