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Softw. Eng. Methodol."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>Automated unit test generation has been widely studied, with Large Language Models (LLMs) recently showing significant potential. LLMs like GPT-4, trained in vast text and code data, excel in various code-related tasks, including unit test generation. However, existing LLM-based approaches often focus solely on the context within the code itself, such as referenced variables, while neglecting broader task-specific contexts, such as the utility of referring to existing tests of relevant methods in unit test generation. Moreover, in the context of unit test generation, these tools prioritize high code coverage, often at the expense of practical usability, correctness, and maintainability.<\/jats:p>\n                  <jats:p>\n                    In response, we propose\n                    <jats:italic toggle=\"yes\">Reference-Based Retrieval Augmentation<\/jats:italic>\n                    , a novel mechanism that extends LLM-based Retrieval-Augmented Generation (RAG) to retrieve relevant information by considering task-specific context. In the unit test generation task, for a given focal method, the\n                    <jats:italic toggle=\"yes\">reference relationships<\/jats:italic>\n                    is defined as the reusability or referentiality of tests between the focal method and other methods. To generate high-quality unit tests for the focal method, the test reference relationships are then used to retrieve relevant methods and their existing unit tests. Specifically, we account for the unique structure of unit tests by dividing the test generation process into\n                    <jats:italic toggle=\"yes\">Given<\/jats:italic>\n                    ,\n                    <jats:italic toggle=\"yes\">When<\/jats:italic>\n                    , and\n                    <jats:italic toggle=\"yes\">Then<\/jats:italic>\n                    phases. When generating unit tests for a focal method, we retrieve pre-existing tests of other relevant methods, which can provide valuable insights for any of the\n                    <jats:italic toggle=\"yes\">Given<\/jats:italic>\n                    ,\n                    <jats:italic toggle=\"yes\">When<\/jats:italic>\n                    , and\n                    <jats:italic toggle=\"yes\">Then<\/jats:italic>\n                    phases. We implement this approach in a tool called\n                    <jats:italic toggle=\"yes\">RefTest<\/jats:italic>\n                    , which sequentially performs preprocessing, test reference retrieval, and unit test generation, using an incremental strategy in which newly generated tests guide the creation of subsequent ones. We evaluated RefTest on 12 open source projects with 1,515 methods, and the results demonstrate that RefTest consistently outperforms existing tools in terms of correctness, completeness, and maintainability of the generated tests.\n                  <\/jats:p>","DOI":"10.1145\/3765758","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T12:41:26Z","timestamp":1764765686000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Reference-Based Retrieval-Augmented Unit Test Generation"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4089-4087","authenticated-orcid":false,"given":"Zhe","family":"Zhang","sequence":"first","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7490-6149","authenticated-orcid":false,"given":"Xingyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6294-326X","authenticated-orcid":false,"given":"Yuanzhang","family":"Lin","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9895-4600","authenticated-orcid":false,"given":"Xiang","family":"Gao","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China and Hangzhou Innovation Institute of Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7654-5574","authenticated-orcid":false,"given":"Hailong","family":"Sun","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China and Hangzhou Innovation Institute of Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4233-4407","authenticated-orcid":false,"given":"Yuan","family":"Yuan","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China and Hangzhou Innovation Institute of Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Saranya Alagarsamy Chakkrit Tantithamthavorn Chetan Arora and Aldeida Aleti. 2024. 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