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In this paper, we propose MLM4DNN, an element-based automated DNN repair method. Unlike previous techniques that focus on post-training adjustments or rely heavily on predefined bug patterns, MLM4DNN repairs DNNs by leveraging a fine-tuned Masked Language Model (MLM) to predict correct fixes for nine predefined key elements in DNNs. We construct a large-scale dataset by masking nine key elements from the correct DNN source code and then force the MLM to restore the correct elements to learn the deep semantics that ensure the normal functionalities of DNNs. Afterwards, a light-weight static analysis tool is designed to filter out low-quality patches to enhance the repair efficiency. We introduce a patch validation method specifically for DNN repair tasks, which consists of three evaluation metrics from different aspects to model the effectiveness of generated patches. We construct a benchmark,\n                    <jats:italic toggle=\"yes\">\n                      Benchmark\n                      <jats:sub>APR4DNN<\/jats:sub>\n                    <\/jats:italic>\n                    , including 51 buggy DNN models and an evaluation tool that outputs the three metrics. We evaluate MLM4DNN against six baselines on\n                    <jats:italic toggle=\"yes\">\n                      Benchmark\n                      <jats:sub>APR4DNN<\/jats:sub>\n                    <\/jats:italic>\n                    , and results show that MLM4DNN outperforms all state-of-the-art baselines, including two dynamic-based and four zero-shot learning-based methods. After applying the fine-tuned MLM design to several prevalent Large Language Models (LLMs), we consistently observe improved performance in DNN repair tasks compared to the original LLMs, which demonstrates the effectiveness of the method proposed in this paper.\n                  <\/jats:p>","DOI":"10.1145\/3715716","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T11:16:02Z","timestamp":1750331762000},"page":"106-129","source":"Crossref","is-referenced-by-count":0,"title":["Element-Based Automated DNN Repair with Fine-Tuned Masked Language Model"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5057-3278","authenticated-orcid":false,"given":"Xu","family":"Wang","sequence":"first","affiliation":[{"name":"Beihang University, State Key Lab of CCSE, Beijing, China"},{"name":"Zhongguancun Laboratory, Beijing, China"},{"name":"Engineering Research Center of Intelligent Computing for Complex Energy Systems, Ministry of Education, Baoding, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0522-4861","authenticated-orcid":false,"given":"Mingming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beihang University, State Key Lab of CCSE, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5630-4810","authenticated-orcid":false,"given":"Xiangxin","family":"Meng","sequence":"additional","affiliation":[{"name":"Beihang University, State Key Lab of CCSE, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8316-1894","authenticated-orcid":false,"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Beijing, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7300-9215","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Beijing, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3473-9703","authenticated-orcid":false,"given":"Chunming","family":"Hu","sequence":"additional","affiliation":[{"name":"Beihang University, State Key Lab of CCSE, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,19]]},"reference":[{"key":"e_1_3_1_2_1","unstructured":"Martin Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S Corrado Andy Davis Jeffrey Dean Matthieu Devin et al. 2016. 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