{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:27:19Z","timestamp":1785421639688,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T00:00:00Z","timestamp":1653091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,5,21]]},"DOI":"10.1145\/3510454.3516865","type":"proceedings-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T16:16:28Z","timestamp":1666196188000},"page":"178-182","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":128,"title":["ReGVD"],"prefix":"10.1145","author":[{"given":"Van-Anh","family":"Nguyen","sequence":"first","affiliation":[{"name":"VNU - University of Science, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dai Quoc","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Oracle Labs, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Van","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Monash University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Trung","family":"Le","sequence":"additional","affiliation":[{"name":"Monash University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan Hung","family":"Tran","sequence":"additional","affiliation":[{"name":"Adobe Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinh","family":"Phung","sequence":"additional","affiliation":[{"name":"Monash University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,19]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Residual gated graph convnets. arXiv preprint arXiv:1711.07553","author":"Bresson Xavier","year":"2017","unstructured":"Xavier Bresson and Thomas Laurent . 2017. Residual gated graph convnets. arXiv preprint arXiv:1711.07553 ( 2017 ). Xavier Bresson and Thomas Laurent. 2017. Residual gated graph convnets. arXiv preprint arXiv:1711.07553 (2017)."},{"key":"e_1_3_2_1_2_1","unstructured":"Kyunghyun Cho Bart van Merri\u00ebnboer Caglar Gulcehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In EMNLP. 1724--1734.  Kyunghyun Cho Bart van Merri\u00ebnboer Caglar Gulcehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In EMNLP. 1724--1734."},{"key":"e_1_3_2_1_3_1","volume-title":"Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555","author":"Clark Kevin","year":"2020","unstructured":"Kevin Clark , Minh-Thang Luong , Quoc V Le , and Christopher D Manning . 2020 . Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020). Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning. 2020. Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020)."},{"key":"e_1_3_2_1_4_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2018 . Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"e_1_3_2_1_6_1","volume-title":"Colin B. Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou.","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 ICLR. 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 ICLR."},{"key":"e_1_3_2_1_7_1","volume-title":"Representation learning on graphs: Methods and applications. preprint arXiv:1709.05584","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton , Rex Ying , and Jure Leskovec . 2017. Representation learning on graphs: Methods and applications. preprint arXiv:1709.05584 ( 2017 ). William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. preprint arXiv:1709.05584 (2017)."},{"key":"e_1_3_2_1_8_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778.  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Lianzhe Huang Dehong Ma Sujian Li Xiaodong Zhang and Houfeng Wang. 2019. Text Level Graph Neural Network for Text Classification. In EMNLP-IJCNLP.  Lianzhe Huang Dehong Ma Sujian Li Xiaodong Zhang and Houfeng Wang. 2019. Text Level Graph Neural Network for Text Classification. In EMNLP-IJCNLP.","DOI":"10.18653\/v1\/D19-1345"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In EMNLP. 1746--1751.  Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In EMNLP. 1746--1751.","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_3_2_1_12_1","volume-title":"Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik","year":"2014","unstructured":"Diederik Kingma and Jimmy Ba . 2014 . Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_13_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In ICLR. Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR."},{"key":"e_1_3_2_1_14_1","unstructured":"Yujia Li Daniel Tarlow Marc Brockschmidt and Richard Zemel. 2016. Gated Graph Sequence Neural Networks. In ICLR.  Yujia Li Daniel Tarlow Marc Brockschmidt and Richard Zemel. 2016. Gated Graph Sequence Neural Networks. In ICLR."},{"key":"e_1_3_2_1_15_1","volume-title":"Vuldeepecker: A deep learning-based system for vulnerability detection. arXiv preprint arXiv:1801.01681","author":"Li Zhen","year":"2018","unstructured":"Zhen Li , Deqing Zou , Shouhuai Xu , Xinyu Ou , Hai Jin , Sujuan Wang , Zhijun Deng , and Yuyi Zhong . 2018 . Vuldeepecker: A deep learning-based system for vulnerability detection. arXiv preprint arXiv:1801.01681 (2018). Zhen Li, Deqing Zou, Shouhuai Xu, Xinyu Ou, Hai Jin, Sujuan Wang, Zhijun Deng, and Yuyi Zhong. 2018. Vuldeepecker: A deep learning-based system for vulnerability detection. arXiv preprint arXiv:1801.01681 (2018)."},{"key":"e_1_3_2_1_16_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv: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. arXiv preprint arXiv:1907.11692 (2019). 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. arXiv preprint arXiv:1907.11692 (2019)."},{"key":"e_1_3_2_1_17_1","volume-title":"Shengyu Fu, and Shujie Liu.","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 . arXiv preprint arXiv:2102.04664 (2021). 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. arXiv preprint arXiv:2102.04664 (2021)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Stephan Neuhaus Thomas Zimmermann Christian Holler and Andreas Zeller. 2007. Predicting vulnerable software components. In ACM CCS. 529--540.  Stephan Neuhaus Thomas Zimmermann Christian Holler and Andreas Zeller. 2007. Predicting vulnerable software components. In ACM CCS. 529--540.","DOI":"10.1145\/1315245.1315311"},{"key":"e_1_3_2_1_20_1","volume-title":"Tu Dinh Nguyen, and Dinh Phung","author":"Nguyen Dai Quoc","year":"2019","unstructured":"Dai Quoc Nguyen , Tu Dinh Nguyen, and Dinh Phung . 2019 . Universal Graph Transformer Self-Attention Networks . arXiv preprint arXiv:1909.11855 (2019). Dai Quoc Nguyen, Tu Dinh Nguyen, and Dinh Phung. 2019. Universal Graph Transformer Self-Attention Networks. arXiv preprint arXiv:1909.11855 (2019)."},{"key":"e_1_3_2_1_21_1","volume-title":"Quaternion Graph Neural Networks. In Asian Conference on Machine Learning.","author":"Nguyen Dai Quoc","year":"2021","unstructured":"Dai Quoc Nguyen , Tu Dinh Nguyen , and Dinh Phung . 2021 . Quaternion Graph Neural Networks. In Asian Conference on Machine Learning. Dai Quoc Nguyen, Tu Dinh Nguyen, and Dinh Phung. 2021. Quaternion Graph Neural Networks. In Asian Conference on Machine Learning."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Viet Hung Nguyen and Le Minh Sang Tran. 2010. Predicting vulnerable software components with dependency graphs. In MetriSec. 1--8.  Viet Hung Nguyen and Le Minh Sang Tran. 2010. Predicting vulnerable software components with dependency graphs. In MetriSec. 1--8.","DOI":"10.1145\/1853919.1853923"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Rebecca Russell Louis Kim Lei Hamilton Tomo Lazovich Jacob Harer Onur Ozdemir Paul Ellingwood and Marc McConley. 2018. Automated vulnerability detection in source code using deep representation learning. In ICMLA.  Rebecca Russell Louis Kim Lei Hamilton Tomo Lazovich Jacob Harer Onur Ozdemir Paul Ellingwood and Marc McConley. 2018. Automated vulnerability detection in source code using deep representation learning. In ICMLA.","DOI":"10.1109\/ICMLA.2018.00120"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_3_2_1_25_1","volume-title":"Evaluating complexity, code churn, and developer activity metrics as indicators of software vulnerabilities","author":"Shin Yonghee","year":"2010","unstructured":"Yonghee Shin , Andrew Meneely , Laurie Williams , and Jason A Osborne . 2010. Evaluating complexity, code churn, and developer activity metrics as indicators of software vulnerabilities . IEEE transactions on software engineering 37 ( 2010 ). Yonghee Shin, Andrew Meneely, Laurie Williams, and Jason A Osborne. 2010. Evaluating complexity, code churn, and developer activity metrics as indicators of software vulnerabilities. IEEE transactions on software engineering 37 (2010)."},{"key":"e_1_3_2_1_26_1","unstructured":"Zonghan Wu Shirui Pan Fengwen Chen Guodong Long Chengqi Zhang and Philip S Yu. 2019. A comprehensive survey on graph neural networks. arXiv:1901.00596.  Zonghan Wu Shirui Pan Fengwen Chen Guodong Long Chengqi Zhang and Philip S Yu. 2019. A comprehensive survey on graph neural networks. arXiv:1901.00596."},{"key":"e_1_3_2_1_27_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful Are Graph Neural Networks?. In ICLR.  Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful Are Graph Neural Networks?. In ICLR."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Liang Yao Chengsheng Mao and Yuan Luo. 2019. Graph convolutional networks for text classification. In AAAI. 7370--7377.  Liang Yao Chengsheng Mao and Yuan Luo. 2019. Graph convolutional networks for text classification. In AAAI. 7370--7377.","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Yufeng Zhang Xueli Yu Zeyu Cui Shu Wu Zhongzhen Wen and Liang Wang. 2020. Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks. In ACL. 334--339.  Yufeng Zhang Xueli Yu Zeyu Cui Shu Wu Zhongzhen Wen and Liang Wang. 2020. Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks. In ACL. 334--339.","DOI":"10.18653\/v1\/2020.acl-main.31"},{"key":"e_1_3_2_1_30_1","volume-title":"Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. In NeurIPS.","author":"Zhou Yaqin","year":"2019","unstructured":"Yaqin Zhou , Shangqing Liu , Jingkai Siow , Xiaoning Du , and Yang Liu . 2019 . Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. In NeurIPS. Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu. 2019. Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. In NeurIPS."}],"event":{"name":"ICSE '22: 44th International Conference on Software Engineering","location":"Pittsburgh Pennsylvania","acronym":"ICSE '22","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS"]},"container-title":["Proceedings of the ACM\/IEEE 44th International Conference on Software Engineering: Companion Proceedings"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3510454.3516865","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3510454.3516865","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:41Z","timestamp":1750188641000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3510454.3516865"}},"subtitle":["revisiting graph neural networks for vulnerability detection"],"short-title":[],"issued":{"date-parts":[[2022,5,21]]},"references-count":29,"alternative-id":["10.1145\/3510454.3516865","10.1145\/3510454"],"URL":"https:\/\/doi.org\/10.1145\/3510454.3516865","relation":{},"subject":[],"published":{"date-parts":[[2022,5,21]]},"assertion":[{"value":"2022-10-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}