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Softw. Eng. Methodol."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    The e\n                    <jats:italic toggle=\"yes\">X<\/jats:italic>\n                    tensible\n                    <jats:italic toggle=\"yes\">M<\/jats:italic>\n                    arkup\n                    <jats:italic toggle=\"yes\">L<\/jats:italic>\n                    anguage (XML) is a file format widely used for data transmission in modern software development. In recent years, embedding SQL statements in XML files (i.e., XML-SQL) has become a popular way for developing applications with database access capability. Typically, XML-SQL code snippets demonstrate similar functionalities and structures, leading to repetitive programming work. Therefore, leveraging pre-trained code models for automated code generation presents a promising way to alleviate duplicated efforts and enhance the efficiency of developing XML-SQL code. However, XML-SQL code has strong domain-specific characteristics that general pre-trained code models typically struggle to fully harness, thereby leading to limited overall performance of general pre-trained code models. In this article, we aim to address the challenge of handling this domain-specific knowledge. First, we propose a code updating task and construct the corresponding TwinXSQL dataset to better evaluate the model\u2019s code generation performance in the XML-SQL domain. Then, we leverage the common characteristics of XML-SQL and other programming languages (i.e., all programming languages impose grammar constraints on behavior) to design a bipartite-grammar\u2013aware training framework (named BGA) for unsupervised pre-training, thereby improving the transfer of general-purpose code models to the XML-SQL domain. Specifically, we divide the XML-SQL code into two types of grammatical components: structure components and value components. During pre-training, we undertake three tasks, each designed to learn the internal information of these grammatical components and the relationships between them, enabling the pre-training process to better incorporate previously unlearned domain-specific knowledge of XML-SQL code. Our experimental results show that our trained model XSQLT5-base (220M) improves accuracy by 13.8% compared to the similarly sized CodeT5-base (220M). Additionally, our experiments reveal that ChatGPT, due to its inability to fully learn the XML-SQL domain knowledge, achieves a much lower generation accuracy even with few-shot samples compared to our XSQLT5-base (220M) model.\n                  <\/jats:p>","DOI":"10.1145\/3731752","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T11:37:17Z","timestamp":1745926637000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Bipartite-Grammar\u2013Aware Pretraining for XML-SQL Code Updating"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1697-3451","authenticated-orcid":false,"given":"Qingyuan","family":"Liang","sequence":"first","affiliation":[{"name":"Key Lab of HCST (PKU), MOE, SCS, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9990-9120","authenticated-orcid":false,"given":"Zeyu","family":"Sun","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Space Integrated Information System, Institute of Software, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2035-0791","authenticated-orcid":false,"given":"Yifan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Lab of HCST (PKU), MOE, SCS, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5318-8473","authenticated-orcid":false,"given":"Zhihao","family":"Gong","sequence":"additional","affiliation":[{"name":"Key Lab of HCST (PKU), MOE, SCS, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5208-4750","authenticated-orcid":false,"given":"Guoqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Lab of HCST (PKU), MOE, SCS, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1821-3170","authenticated-orcid":false,"given":"Yizhou","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Lab of HCST (PKU), MOE, SCS, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8304-7055","authenticated-orcid":false,"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Lab of HCST (PKU), MOE, SCS, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2454-1706","authenticated-orcid":false,"given":"Guangtai","family":"Liang","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co Ltd, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6598-0041","authenticated-orcid":false,"given":"Qianxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co Ltd, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"ChatGPT. 2022. Retrieved from https:\/\/openai.com\/index\/chatgpt\/"},{"key":"e_1_3_1_3_2","unstructured":"ChatGPT-Instruct. 2022. Retrieved from https:\/\/platform.openai.com\/docs\/models\/gpt-3.5-turbo"},{"key":"e_1_3_1_4_2","unstructured":"MySQL. 2024. Retrieved from https:\/\/www.mysql.com\/"},{"key":"e_1_3_1_5_2","unstructured":"Oracle Database. 2024. Retrieved from https:\/\/www.oracle.com\/database\/"},{"key":"e_1_3_1_6_2","unstructured":"Wasi Uddin Ahmad Saikat Chakraborty Baishakhi Ray and Kai-Wei Chang. 2021. Unified pre-training for program understanding and generation. arXiv:2103.06333. Retrieved from https:\/\/arxiv.org\/abs\/2103.06333"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3188898"},{"key":"e_1_3_1_8_2","unstructured":"Jacob Austin Augustus Odena Maxwell Nye Maarten Bosma Henryk Michalewski David Dohan Ellen Jiang Carrie Cai Michael Terry Quoc Le et al. 2021. Program synthesis with large language models. arXiv:2108.07732. 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