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Security patches in open-source software, providing security fixes to identified vulnerabilities, are crucial in protecting against cyberattacks.
PatchNet: A Tool for Deep Patch Classification
Thong Hoang, Julia Lawall, Richard Jayadi Oentaryo, Yuan Tian, and David Lo. 2019 · 1903
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An Empirical Model to Predict Security Vulnerabilities Using Code Complexity Metrics. In Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (Kaiserslautern, Germany). ACM, New York, NY, USA, 315–317
Yonghee Shin and Laurie Williams. 2008 · 2008
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CVE-2010-5329
2010 · 2010
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Thomas Zimmermann, Nachiappan Nagappan, and Laurie Williams. 2010 · 2010
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Automated Topic Naming to Support Cross-Project Analysis of Software Maintenance Activities. In Proceedings of the 8th Working Conference on Mining Software Repositories (Waikiki, Honolulu, HI, USA) (MSR ’11) . Association for Computing Machinery, New York, NY, USA, 163–172
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Evaluating Complexity, Code Churn, and Developer Activity Metrics As Indicators of Software Vulnerabilities
Yonghee Shin, Andrew Meneely, Laurie Williams, and Jason A. Osborne. 2011 · 2011
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Mining of massive datasets
Jeffrey David Ullman. 2011 · 2011
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Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems . 3111–3119
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Convolutional Neural Networks for Sentence Classification
Yoon Kim. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Security and Emotion: Sentiment Analysis of Security Discussions on GitHub. In Proceedings of the 11th Working Conference on Mining Software Repositories (Hyderabad, India) (MSR 2014) . Association for Computing Machinery, New York, NY, USA, 348–351
Daniel Pletea, Bogdan Vasilescu, and Alexander Serebrenik. 2014 · 2014
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A Large Scale Study of Programming Languages and Code Quality in Github. In Proceedings of the 22Nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (Hong Kong, China). ACM, New York, NY, USA, 155–165
Baishakhi Ray, Daryl Posnett, Vladimir Filkov, and Premkumar Devanbu. 2014 · 2014
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Code Completion with Statistical Language Models. In Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation (Edinburgh, United Kingdom). ACM, New York, NY, USA, 419–428
Veselin Raychev, Martin Vechev, and Eran Yahav. 2014 · 2014
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CVE-2015-8952
2015 · 2015
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems
Martín Abadi, Ashish Agarwal, Paul Barham, and et al. 2015 · 2015
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Gated graph sequence neural networks
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A Critical Review of Recurrent Neural Networks for Sequence Learning
Zachary C. Lipton, John Berkowitz, and Charles Elkan. 2015 · 2015
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Vccfinder: Finding potential vulnerabilities in open-source projects to assist code audits. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security . ACM, 426–437
Henning Perl, Sergej Dechand, Matthew Smith, Daniel Arp, Fabian Yamaguchi, Konrad Rieck, Sascha Fahl, and Yasemin Acar. 2015 · 2015
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Deep Learning for Just-in-Time Defect Prediction. In 2015 IEEE International Conference on Software Quality, Reliability and Security . 17–26
X. Yang, D. Lo, X. Xia, Y. Zhang, and J. Sun. 2015 · 2015
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016 · 2016
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Deep API Learning. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (Seattle, WA, USA). ACM, New York, NY, USA, 631–642
Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, and Sunghun Kim. 2016 · 2016
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Learning Unified Features from Natural and Programming Languages for Locating Buggy Source Code. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (New York, New York, USA). AAAI Press, 1606–1612
Xuan Huo, Ming Li, and Zhi-Hua Zhou. 2016 · 2016
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Convolutional Neural Networks over Tree Structures for Programming Language Processing.. In AAAI , Vol. 2. 4
Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. 2016 · 2016
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Automatically learning semantic features for defect prediction. In Proceedings of the 38th International Conference on Software Engineering . ACM, 297–308
Song Wang, Taiyue Liu, and Lin Tan. 2016 · 2016
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Deep learning code fragments for code clone detection. In 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE) . 87–98
M. White, M. Tufano, C. Vendome, and D. Poshyvanyk. 2016 · 2016
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CVE-2017-5638
2017a · 2017
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CVE-2017-7187
2017b · 2017
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Machine Learning at SourceClear
2017 · 2017
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Silently (or obliviously) partially-fixed CONFIG_STRICT_DEVMEM bypass
2017 · 2017
Cited alongside, same era.
DeepBugs: A Learning Approach to Name-based Bug Detection
Michael Pradel and Koushik Sen. 2018 · 2018
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Automated vulnerability detection in source code using deep representation learning. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) . IEEE, 757–762
Rebecca Russell, Louis Kim, Lei Hamilton, Tomo Lazovich, Jacob Harer, Onur Ozdemir, Paul Ellingwood, and Marc McConley. 2018a · 2018
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Automated Vulnerability Detection in Source Code Using Deep Representation Learning
Rebecca L. Russell, Louis Y. Kim, Lei H. Hamilton, Tomo Lazovich, Jacob A. Harer, Onur Ozdemir, Paul M. Ellingwood, and Marc W. McConley. 2018b · 2018
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An Empirical Investigation into Learning Bug-fixing Patches in the Wild via Neural Machine Translation. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (Montpellier, France) (ASE 2018) . ACM, New York, NY, USA, 832–837
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. 2018 · 2018
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SentiCR: A customized sentiment analysis tool for code review interactions. In 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) . 106–111
T. Ahmed, A. Bosu, A. Iqbal, and S. Rahimi. 2017 · 2017
Cited alongside, same era.
RiskTeller: Predicting the Risk of Cyber Incidents. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 1299–1311
Leyla Bilge, Yufei Han, and Matteo Dell’Amico. 2017 · 2017
Cited alongside, same era.
Very Deep Convolutional Networks for Text Classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers . Association for Computational Linguistics, Valencia, Spain, 1107–1116
Alexis Conneau, Holger Schwenk, Loïc Barrault, and Yann Lecun. 2017 · 2017
Cited alongside, same era.
Automatic feature learning for vulnerability prediction
Hoa Khanh Dam, Truyen Tran, Trang Pham, Shien Wee Ng, John Grundy, and Aditya Ghose. 2017 · 2017
Cited alongside, same era.
DeepFix: Fixing Common C Language Errors by Deep Learning. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (San Francisco, California, USA) (AAAI’17) . AAAI Press, 1345–1351
Rahul Gupta, Soham Pal, Aditya Kanade, and Shirish Shevade. 2017 · 2017
Cited alongside, same era.
A Large-Scale Empirical Study of Security Patches. 2201–2215
Frank Li and Vern Paxson. 2017 · 2017
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Deep Learning Type Inference
Earl T. Barr Vincent Hellendoorn, Christian Bird and Miltiadis Allamanis. 2018 · 2018
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The state of open source security - 2019
[n.d.] · 2019
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Checkmarx
2019 · 2019
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CVEDetails - CVE Number by Date
2019 · 2019
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Pygments
2019 · 2019
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Software Assurance Reference Dataset
2019 · 2019
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The state of octoverse
2019 · 2019
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Detecting "0-Day" Vulnerability: An Empirical Study of Secret Security Patch in OSS. In 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) . 485–492
X. Wang, K. Sun, A. Batcheller, and S. Jajodia. 2019 · 2019
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Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks. In Advances in Neural Information Processing Systems . 10197–10207
Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu. 2019 · 2019
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Open Sourced Security and License Management - 2020
[n.d.] · 2020
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National Vulnerability Database - 2020
2020 · 2020
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VulnDB - 2020
2020 · 2020
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HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DETECT AND FIX BUGS IN PROGRAMS. In International Conference on Learning Representations
Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, and Ke Wang. 2020 · 2020
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DLfix: Context-based code transformation learning for automated program repair. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering . 602–614
Yi Li, Shaohua Wang, and Tien N Nguyen. 2020 · 2020
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A Unified Framework to Learn Program Semantics with Graph Neural Networks. In 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 1364–1366
Shangqing Liu. 2020 · 2020
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ATOM: Commit message generation based on abstract syntax tree and hybrid ranking
Shangqing Liu, Cuiyun Gao, Sen Chen, Nie Lun Yiu, and Yang Liu. 2020 · 2020
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CORE: Automating Review Recommendation for Code Changes. In 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 284–295
Jing Kai Siow, Cuiyun Gao, Lingling Fan, Sen Chen, and Yang Liu. 2020 · 2020
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Retrieval-Augmented Generation for Code Summarization via Hybrid {GNN}. In International Conference on Learning Representations
Shangqing Liu, Yu Chen, Xiaofei Xie, Jing Kai Siow, and Yang Liu. 2021 · 2021
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