{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T11:55:07Z","timestamp":1791460507565,"version":"4.3.4"},"publisher-location":"New York, NY, USA","reference-count":69,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202420"],"award-info":[{"award-number":["62202420"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,4,12]]},"DOI":"10.1145\/3597503.3639142","type":"proceedings-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T12:43:26Z","timestamp":1712925806000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["Pre-training by Predicting Program Dependencies for Vulnerability Analysis Tasks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1981-1626","authenticated-orcid":false,"given":"Zhongxin","family":"Liu","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4575-8937","authenticated-orcid":false,"given":"Zhijie","family":"Tang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4323-8951","authenticated-orcid":false,"given":"Junwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University, 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, Shenzhen, China"}],"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, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2023. National vulnerability database. https:\/\/nvd.nist\/."},{"key":"e_1_3_2_1_2_1","unstructured":"2023. Our GitHub repository. https:\/\/github.com\/ZJU-CTAG\/PDBERT."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","unstructured":"2023. Our replication package. 10.5281\/zenodo.8196552","DOI":"10.5281\/zenodo.8196552"},{"key":"e_1_3_2_1_4_1","unstructured":"2023. VulDB. https:\/\/vuldb.com\/?kb.sources."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2017.04.005"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.25"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.211"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCC50000.2020.9219568"},{"key":"e_1_3_2_1_9_1","volume-title":"Longformer: The Long-Document Transformer. CoRR abs\/2004.05150","author":"Beltagy Iz","year":"2020","unstructured":"Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020. Longformer: The Long-Document Transformer. CoRR abs\/2004.05150 (2020). arXiv:2004.05150 https:\/\/arxiv.org\/abs\/2004.05150"},{"key":"e_1_3_2_1_10_1","volume-title":"Language Models Are Few-Shot Learners. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Brown Tom B.","year":"2020","unstructured":"Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models Are Few-Shot Learners. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462840"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510219"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549162"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2021.3087402"},{"key":"e_1_3_2_1_15_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning. PMLR, 1597--1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning. PMLR, 1597--1607."},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of the 8th International Conference on Learning Representations.","author":"Clark Kevin","unstructured":"Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020. ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. In Proceedings of the 8th International Conference on Learning Representations."},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4171--4186","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4171--4186."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.436"},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings of the 17th International Conference on Mining Software Repositories. 508--512","author":"Fan Jiahao","unstructured":"Jiahao Fan, Yi Li, Shaohua Wang, and Tien N. Nguyen. 2020. A C\/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries. In Proceedings of the 17th International Conference on Mining Software Repositories. 508--512."},{"key":"e_1_3_2_1_20_1","volume-title":"CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020 (Findings of ACL","volume":"1547","author":"Feng Zhangyin","year":"2020","unstructured":"Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020. CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020 (Findings of ACL, Vol. EMNLP 2020). 1536--1547."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/24039.24041"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.31"},{"key":"e_1_3_2_1_23_1","volume-title":"Comparing Two K-category Assignments by a K-category Correlation Coefficient. Computational biology and chemistry 28, 5--6","author":"Gorodkin Jan","year":"2004","unstructured":"Jan Gorodkin. 2004. Comparing Two K-category Assignments by a K-category Correlation Coefficient. Computational biology and chemistry 28, 5--6 (2004), 367--374."},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. 6645--6649","author":"Graves Alex","unstructured":"Alex Graves, Abdel-rahman Mohamed, and Geoffrey E. Hinton. 2013. Speech recognition with deep recurrent neural networks. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. 6645--6649."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.499"},{"key":"e_1_3_2_1_26_1","volume-title":"Proceedings of the 9th International Conference on Learning Representations.","author":"Guo Daya","year":"2021","unstructured":"Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin B. Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. 2021. GraphCodeBERT: Pre-training Code Representations with Data Flow. In Proceedings of the 9th International Conference on Learning Representations."},{"key":"e_1_3_2_1_27_1","volume-title":"Long-Coder: A Long-Range Pre-trained Language Model for Code Completion. In International Conference on Machine Learning, ICML 2023 (Proceedings of Machine Learning Research","volume":"12107","author":"Guo Daya","unstructured":"Daya Guo, Canwen Xu, Nan Duan, Jian Yin, and Julian J. McAuley. 2023. Long-Coder: A Long-Range Pre-trained Language Model for Code Completion. In International Conference on Machine Learning, ICML 2023 (Proceedings of Machine Learning Research, Vol. 202). PMLR, 12098--12107. https:\/\/proceedings.mlr.press\/v202\/guo23j.html"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSME.2017.52"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN55064.2022.9892280"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3551349.3556912"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.482"},{"key":"e_1_3_2_1_32_1","volume-title":"Treebert: A Tree-Based Pre-Trained Model for Programming Language. In Uncertainty in Artificial Intelligence. 54--63.","author":"Jiang Xue","year":"2021","unstructured":"Xue Jiang, Zhuoran Zheng, Chen Lyu, Liang Li, and Lei Lyu. 2021. Treebert: A Tree-Based Pre-Trained Model for Programming Language. In Uncertainty in Artificial Intelligence. 54--63."},{"key":"e_1_3_2_1_33_1","volume-title":"Learning and Evaluating Contextual Embedding of Source Code. In International Conference on Machine Learning. 5110--5121","author":"Kanade Aditya","year":"2020","unstructured":"Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi. 2020. Learning and Evaluating Contextual Embedding of Source Code. In International Conference on Machine Learning. 5110--5121."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380342"},{"key":"e_1_3_2_1_35_1","volume-title":"Proceedings of the 3rd International Conference on Learning Representations, Yoshua Bengio and Yann LeCun (Eds.).","author":"Diederik","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In Proceedings of the 3rd International Conference on Learning Representations, Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings of the 19th International Conference on Mining Software Repositories. 621--633","author":"Minh Le Triet Huynh","unstructured":"Triet Huynh Minh Le and M. Ali Babar. 2022. On the Use of Fine-Grained Vulnerable Code Statements for Software Vulnerability Assessment Models. In Proceedings of the 19th International Conference on Mining Software Repositories. 621--633."},{"key":"e_1_3_2_1_37_1","volume-title":"A Survey on Data-driven Software Vulnerability Assessment and Prioritization. ACM Comput. Surv. 55, 5","author":"Minh Le Triet Huynh","year":"2023","unstructured":"Triet Huynh Minh Le, Huaming Chen, and Muhammad Ali Babar. 2023. A Survey on Data-driven Software Vulnerability Assessment and Prioritization. ACM Comput. Surv. 55, 5 (2023), 100:1--100:39."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678622"},{"key":"e_1_3_2_1_39_1","volume-title":"Proceedings of the 2019 IEEE\/ACM 16th International Conference on Mining Software Repositories. 371--382","author":"Minh Le Triet Huynh","year":"2019","unstructured":"Triet Huynh Minh Le, Bushra Sabir, and Muhammad Ali Babar. 2019. Automated Software Vulnerability Assessment with Concept Drift. In Proceedings of the 2019 IEEE\/ACM 16th International Conference on Mining Software Repositories. 371--382."},{"key":"e_1_3_2_1_40_1","volume-title":"Agustin Dal Lago, et al","author":"Li Yujia","year":"2022","unstructured":"Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, R\u00e9mi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al. 2022. Competition-level code generation with alphacode. Science 378, 6624 (2022), 1092--1097."},{"key":"e_1_3_2_1_41_1","volume-title":"Proceedings of the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 292--303","author":"Li Yi","unstructured":"Yi Li, Shaohua Wang, and Tien N. Nguyen. 2021. Vulnerability detection with fine-grained interpretations. In Proceedings of the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 292--303."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549081"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3051525"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23158"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3324884.3416591"},{"key":"e_1_3_2_1_46_1","volume-title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR abs\/1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR abs\/1907.11692 (2019). arXiv:1907.11692"},{"key":"e_1_3_2_1_47_1","volume-title":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks","author":"Lu Shuai","year":"2021","unstructured":"Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin B. Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, and Shujie Liu. 2021. CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00041"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510096"},{"key":"e_1_3_2_1_50_1","first-page":"1","article-title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res. 21, 140 (2020), 1--67.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_1_51_1","volume-title":"Database and Expert Systems Applications - DEXA 2018 International Workshops, BDMICS, BIOKDD, and TIR (Communications in Computer and Information Science","author":"Ruohonen Jukka","unstructured":"Jukka Ruohonen and Ville Lepp\u00e4nen. 2018. Toward Validation of Textual Information Retrieval Techniques for Software Weaknesses. In Database and Expert Systems Applications - DEXA 2018 International Workshops, BDMICS, BIOKDD, and TIR (Communications in Computer and Information Science, Vol. 903). 265--277."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00120"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1162"},{"key":"e_1_3_2_1_55_1","volume-title":"Reasoning With Neural Tensor Networks for Knowledge Base Completion. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems","author":"Socher Richard","year":"2013","unstructured":"Richard Socher, Danqi Chen, Christopher D. Manning, and Andrew Y. Ng. 2013. Reasoning With Neural Tensor Networks for Knowledge Base Completion. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. 926--934."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2018.09.039"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3417058"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510067"},{"key":"e_1_3_2_1_59_1","volume-title":"Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017. 5998--6008."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2020.3023664"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510050"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-naacl.80"},{"key":"e_1_3_2_1_63_1","volume-title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 8696--8708","author":"Wang Yue","unstructured":"Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi. 2021. CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 8696--8708."},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2014.44"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3551349.3556955"},{"key":"e_1_3_2_1_66_1","volume-title":"Proceedings of the 36th IEEE\/ACM International Conference on Automated Software Engineering. 705--716","author":"Zhou Jiayuan","unstructured":"Jiayuan Zhou, Michael Pacheco, Zhiyuan Wan, Xin Xia, David Lo, Yuan Wang, and Ahmed E. Hassan. 2021. Finding a Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes. In Proceedings of the 36th IEEE\/ACM International Conference on Automated Software Engineering. 705--716."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSME52107.2021.00044"},{"key":"e_1_3_2_1_68_1","volume-title":"Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems","author":"Zhou Yaqin","year":"2019","unstructured":"Yaqin Zhou, Shangqing Liu, Jing Kai Siow, Xiaoning Du, and Yang Liu. 2019. Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019. 10197--10207."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3468854","article-title":"Spi: Automated Identification of Security Patches via Commits","volume":"31","author":"Zhou Yaqin","year":"2021","unstructured":"Yaqin Zhou, Jing Kai Siow, Chenyu Wang, Shangqing Liu, and Yang Liu. 2021. Spi: Automated Identification of Security Patches via Commits. ACM Transactions on Software Engineering and Methodology (TOSEM) 31, 1 (2021), 1--27.","journal-title":"ACM Transactions on Software Engineering and Methodology (TOSEM)"},{"key":"e_1_3_2_1_70_1","first-page":"2224","article-title":"\u03bcVulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection","volume":"18","author":"Zou Deqing","year":"2019","unstructured":"Deqing Zou, Sujuan Wang, Shouhuai Xu, Zhen Li, and Hai Jin. 2019. \u03bcVulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection. IEEE Transactions on Dependable and Secure Computing 18, 5 (2019), 2224--2236.","journal-title":"IEEE Transactions on Dependable and Secure Computing"}],"event":{"name":"ICSE '24: IEEE\/ACM 46th International Conference on Software Engineering","location":"Lisbon Portugal","acronym":"ICSE '24","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS","Faculty of Engineering of University of Porto"]},"container-title":["Proceedings of the IEEE\/ACM 46th International Conference on Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597503.3639142","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3597503.3639142","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T18:49:12Z","timestamp":1750272552000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597503.3639142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,12]]},"references-count":69,"alternative-id":["10.1145\/3597503.3639142","10.1145\/3597503"],"URL":"https:\/\/doi.org\/10.1145\/3597503.3639142","relation":{},"subject":[],"published":{"date-parts":[[2024,4,12]]},"assertion":[{"value":"2024-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}