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Hoppity: Learning graph transformations to detect and fix bugs in programs . In International Conference on Learning Representations (ICLR). Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, and Ke Wang. 2020. Hoppity: Learning graph transformations to detect and fix bugs in programs. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3379597.3387501"},{"key":"e_1_3_2_1_18_1","volume-title":"Antonino Sabetta, Dario Di Nucci, and Damian A Tamburri.","author":"Fehrer Therese","year":"2021","unstructured":"Therese Fehrer , Roc\u00edo Cabrera Lozoya , Antonino Sabetta, Dario Di Nucci, and Damian A Tamburri. 2021 . Detecting Security Fixes in Open-Source Repositories using Static Code Analyzers . arXiv preprint arXiv:2105.03346. Therese Fehrer, Roc\u00edo Cabrera Lozoya, Antonino Sabetta, Dario Di Nucci, and Damian A Tamburri. 2021. 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Advances in Neural Information Processing Systems, 31 (2018)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3140868"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00107"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSC.2016.33"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2019.00011"},{"key":"e_1_3_2_1_27_1","volume-title":"Opennmt: Open-source toolkit for neural machine translation. arXiv preprint arXiv:1701.02810.","author":"Klein Guillaume","year":"2017","unstructured":"Guillaume Klein , Yoon Kim , Yuntian Deng , Jean Senellart , and Alexander M Rush . 2017 . Opennmt: Open-source toolkit for neural machine translation. arXiv preprint arXiv:1701.02810. Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M Rush. 2017. 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PolyFax: A Toolkit for Characterizing Multi-Language Software. In ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC\/FSE). Wen Li, Li Li, and Haipeng Cai. 2022. PolyFax: A Toolkit for Characterizing Multi-Language Software. In ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC\/FSE)."},{"key":"e_1_3_2_1_31_1","volume-title":"PolyCruise: A Cross-Language Dynamic Information Flow Analysis. In 31st USENIX Security Symposium (USENIX Security 22)","author":"Li Wen","year":"2022","unstructured":"Wen Li , Jiang Ming , Xiapu Luo , and Haipeng Cai . 2022 . PolyCruise: A Cross-Language Dynamic Information Flow Analysis. In 31st USENIX Security Symposium (USENIX Security 22) . Boston, MA. 2513\u20132530. isbn:978-1-939133-31-1 Wen Li, Jiang Ming, Xiapu Luo, and Haipeng Cai. 2022. PolyCruise: A Cross-Language Dynamic Information Flow Analysis. 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VulDeeLocator: A Deep Learning-based Fine-grained Vulnerability Detector. arXiv preprint arXiv:2001.02350."},{"key":"e_1_3_2_1_35_1","article-title":"SySeVR: A framework for using deep learning to detect software vulnerabilities","author":"Li Zhen","year":"2021","unstructured":"Zhen Li , Deqing Zou , Shouhuai Xu , Hai Jin , Yawei Zhu , and Zhaoxuan Chen . 2021 . SySeVR: A framework for using deep learning to detect software vulnerabilities . IEEE Transactions on Dependable and Secure Computing. Zhen Li, Deqing Zou, Shouhuai Xu, Hai Jin, Yawei Zhu, and Zhaoxuan Chen. 2021. SySeVR: A framework for using deep learning to detect software vulnerabilities. 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Deep learning-based vulnerable function detection: A benchmark . In International Conference on Information and Communications Security. 219\u2013232 . Guanjun Lin, Wei Xiao, Jun Zhang, and Yang Xiang. 2019. Deep learning-based vulnerable function detection: A benchmark. In International Conference on Information and Communications Security. 219\u2013232."},{"key":"e_1_3_2_1_38_1","article-title":"Software Vulnerability Discovery via Learning Multi-domain Knowledge Bases","author":"Lin Guanjun","year":"2019","unstructured":"Guanjun Lin , Jun Zhang , Wei Luo , Lei Pan , Olivier De Vel , Paul Montague , and Yang Xiang . 2019 . Software Vulnerability Discovery via Learning Multi-domain Knowledge Bases . IEEE Transactions on Dependable and Secure Computing. Guanjun Lin, Jun Zhang, Wei Luo, Lei Pan, Olivier De Vel, Paul Montague, and Yang Xiang. 2019. Software Vulnerability Discovery via Learning Multi-domain Knowledge Bases. IEEE Transactions on Dependable and Secure Computing.","journal-title":"IEEE Transactions on Dependable and Secure Computing."},{"key":"e_1_3_2_1_39_1","unstructured":"Linux Utilities. 2022. The Linux Diff tool. https:\/\/www.man7.org\/linux\/man-pages\/man1\/diff.1.html \t\t\t\t  Linux Utilities. 2022. The Linux Diff tool. https:\/\/www.man7.org\/linux\/man-pages\/man1\/diff.1.html"},{"key":"e_1_3_2_1_40_1","unstructured":"National Institute of Standards and Technology (NIST). 2022. National Vulnerability Database (NVD). https:\/\/nvd.nist.gov \t\t\t\t  National Institute of Standards and Technology (NIST). 2022. National Vulnerability Database (NVD). https:\/\/nvd.nist.gov"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/SANER48275.2020.9054851"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2021.106614"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Yu Nong Yuzhe Ou Michael Pradel Feng Chen and Haipeng Cai. 2022. Peer-Evaluated Artifact Package for \u201cGenerating Realistic Vulnerabilities via Neural Code Editing: An Empirical Study\". https:\/\/zenodo.org\/record\/7048525 \t\t\t\t  Yu Nong Yuzhe Ou Michael Pradel Feng Chen and Haipeng Cai. 2022. Peer-Evaluated Artifact Package for \u201cGenerating Realistic Vulnerabilities via Neural Code Editing: An Empirical Study\". https:\/\/zenodo.org\/record\/7048525","DOI":"10.1145\/3540250.3549128"},{"key":"e_1_3_2_1_44_1","first-page":"297","article-title":"Report on the static analysis tool exposition (sate) iv","volume":"500","author":"Okun Vadim","year":"2013","unstructured":"Vadim Okun , Aurelien Delaitre , and Paul E Black . 2013 . Report on the static analysis tool exposition (sate) iv . NIST Special Publication , 500 (2013), 297 . Vadim Okun, Aurelien Delaitre, and Paul E Black. 2013. Report on the static analysis tool exposition (sate) iv. NIST Special Publication, 500 (2013), 297.","journal-title":"NIST Special Publication"},{"key":"e_1_3_2_1_45_1","volume-title":"Comparing the Attention of Humans with Neural Models of Code. In IEEE\/ACM International Conference on Automated Software Engineering (ASE).","author":"Paltenghi Matteo","year":"2021","unstructured":"Matteo Paltenghi and Michael Pradel . 2021 . Thinking Like a Developer? Comparing the Attention of Humans with Neural Models of Code. In IEEE\/ACM International Conference on Automated Software Engineering (ASE). Matteo Paltenghi and Michael Pradel. 2021. Thinking Like a Developer? Comparing the Attention of Humans with Neural Models of Code. In IEEE\/ACM International Conference on Automated Software Engineering (ASE)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468623"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMSWA.2006.1665217"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460348"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2021.106552"},{"key":"e_1_3_2_1_50_1","unstructured":"Sebastian Ruder. 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747. \t\t\t\t  Sebastian Ruder. 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747."},{"key":"e_1_3_2_1_51_1","unstructured":"Abigail See Peter J Liu and Christopher D Manning. 2017. Get to the point: Summarization with pointer-generator networks. arXiv preprint arXiv:1704.04368. \t\t\t\t  Abigail See Peter J Liu and Christopher D Manning. 2017. Get to the point: Summarization with pointer-generator networks. arXiv preprint arXiv:1704.04368."},{"key":"e_1_3_2_1_52_1","unstructured":"Shape Security Inc.. 2022. Shift Parser. https:\/\/shift-ast.org\/parser.html \t\t\t\t  Shape Security Inc.. 2022. Shift Parser. https:\/\/shift-ast.org\/parser.html"},{"key":"e_1_3_2_1_53_1","unstructured":"Peter Silberman and Richard Johnson. 2004. A comparison of buffer overflow prevention implementations and weaknesses. IDEFENSE August. \t\t\t\t  Peter Silberman and Richard Johnson. 2004. A comparison of buffer overflow prevention implementations and weaknesses. IDEFENSE August."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3387940.3392181"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340544"},{"key":"e_1_3_2_1_56_1","volume-title":"Probability and statistics for engineers and scientists. 5","author":"Walpole Ronald E","unstructured":"Ronald E Walpole , Raymond H Myers , Sharon L Myers , and Keying Ye. 1993. Probability and statistics for engineers and scientists. 5 , Macmillan New York . Ronald E Walpole, Raymond H Myers, Sharon L Myers, and Keying Ye. 1993. Probability and statistics for engineers and scientists. 5, Macmillan New York."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN48987.2021.00030"},{"key":"e_1_3_2_1_58_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryGs6iA5Km","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2019 . How Powerful are Graph Neural Networks? In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryGs6iA5Km Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks? In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryGs6iA5Km"},{"key":"e_1_3_2_1_59_1","unstructured":"Fabian Yamaguchi. 2022. A platform for robust analysis of C\/C++ code. https:\/\/joern.readthedocs.io\/en\/latest\/installation.html \t\t\t\t  Fabian Yamaguchi. 2022. A platform for robust analysis of C\/C++ code. https:\/\/joern.readthedocs.io\/en\/latest\/installation.html"},{"key":"e_1_3_2_1_60_1","unstructured":"Ziyu Yao Frank F Xu Pengcheng Yin Huan Sun and Graham Neubig. 2021. Learning Structural Edits via Incremental Tree Transformations. arXiv preprint arXiv:2101.12087. \t\t\t\t  Ziyu Yao Frank F Xu Pengcheng Yin Huan Sun and Graham Neubig. 2021. Learning Structural Edits via Incremental Tree Transformations. arXiv preprint arXiv:2101.12087."},{"key":"e_1_3_2_1_61_1","volume-title":"International Conference on Machine Learning. 10799\u201310808","author":"Yasunaga Michihiro","year":"2020","unstructured":"Michihiro Yasunaga and Percy Liang . 2020 . Graph-based, self-supervised program repair from diagnostic feedback . In International Conference on Machine Learning. 10799\u201310808 . Michihiro Yasunaga and Percy Liang. 2020. Graph-based, self-supervised program repair from diagnostic feedback. In International Conference on Machine Learning. 10799\u201310808."},{"key":"e_1_3_2_1_62_1","unstructured":"Michihiro Yasunaga and Percy Liang. 2021. Break-It-Fix-It: Unsupervised Learning for Program Repair. arXiv preprint arXiv:2106.06600. \t\t\t\t  Michihiro Yasunaga and Percy Liang. 2021. Break-It-Fix-It: Unsupervised Learning for Program Repair. arXiv preprint arXiv:2106.06600."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3428230"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICAICA52286.2021.9497888"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-SEIP52600.2021.00020"},{"key":"e_1_3_2_1_66_1","volume-title":"Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks. arXiv preprint arXiv:1909.03496.","author":"Zhou Yaqin","year":"2019","unstructured":"Yaqin Zhou , Shangqing Liu , Jingkai Siow , Xiaoning Du , and Yang Liu . 2019 . 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