{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T16:19:52Z","timestamp":1792253992576,"version":"4.4.0"},"reference-count":77,"publisher":"Association for Computing Machinery (ACM)","issue":"FSE","license":[{"start":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:00:00Z","timestamp":1750291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Softw. Eng."],"published-print":{"date-parts":[[2025,6,19]]},"abstract":"<jats:p>\n                    Unit testing is crucial for software development and maintenance. Effective unit testing ensures and improves software quality, but writing unit tests is time-consuming and labor-intensive. Recent studies have proposed deep learning (DL) techniques or large language models (LLMs) to automate unit test generation. These models are usually trained or fine-tuned on large-scale datasets. Despite growing awareness of the importance of data quality, there has been limited research on the quality of datasets used for test generation. To bridge this gap, we systematically examine the impact of noise on the performance of learning-based test generation models. We first apply the open card sorting method to analyze the most popular and largest test generation dataset, Methods2Test, to categorize eight distinct types of noise. Further, we conduct detailed interviews with 17 domain experts to validate and assess the importance, reasonableness, and correctness of the noise taxonomy. Then, we propose\n                    <jats:sc>CleanTest<\/jats:sc>\n                    , an automated noise-cleaning framework designed to improve the quality of test generation datasets.\n                    <jats:sc>CleanTest<\/jats:sc>\n                    comprises three filters: a rule-based syntax filter, a rule-based relevance filter, and a model-based coverage filter. To evaluate its effectiveness, we apply\n                    <jats:sc>CleanTest<\/jats:sc>\n                    on two widely-used test generation datasets, i.e., Methods2Test and Atlas. Our findings indicate that 43.52% and 29.65% of datasets contain noise, highlighting its prevalence. Finally, we conduct comparative experiments using four LLMs (i.e., CodeBERT, AthenaTest, StarCoder, and CodeLlama7B) to assess the impact of noise on test generation performance. The results show that filtering noise positively influences the test generation ability of the models. Fine-tuning the four LLMs with the filtered Methods2Test dataset, on average, improves its performance by 67% in branch coverage, using the Defects4J benchmark. For the Atlas dataset, the four LLMs improve branch coverage by 39%. Additionally, filtering noise improves bug detection performance, resulting in a 21.42% increase in bugs detected by the generated tests.\n                  <\/jats:p>","DOI":"10.1145\/3715778","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T11:15:34Z","timestamp":1750331734000},"page":"1293-1316","source":"Crossref","is-referenced-by-count":12,"title":["Less Is More: On the Importance of Data Quality for Unit Test Generation"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4323-8951","authenticated-orcid":false,"given":"Junwei","family":"Zhang","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\/0000-0003-0093-3292","authenticated-orcid":false,"given":"Xing","family":"Hu","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\/0009-0006-2695-2968","authenticated-orcid":false,"given":"Shan","family":"Gao","sequence":"additional","affiliation":[{"name":"Huawei, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6302-3256","authenticated-orcid":false,"given":"Xin","family":"Xia","sequence":"additional","affiliation":[{"name":"Huawei, 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, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2615-9792","authenticated-orcid":false,"given":"Shanping","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang University, The State Key Laboratory of Blockchain and Data Security, 0000-0003-2615-9792, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,19]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.INFSOF.2024.107565"},{"issue":"7","key":"e_1_3_1_3_2","first-page":"64","article-title":"Likert scales and data analyses","volume":"40","author":"Allen I Elaine","year":"2007","unstructured":"I Elaine Allen and Christopher A Seaman. 2007. 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