Detecting privacy-sensitive code changes with language modeling
Authors: 
Gökalp Demirci, Vijayaraghavan Murali, Imad Ahmad, Rajeev Rao, Gareth Ari AyeAuthors Info & Claims
Pages 762 - 763
Abstract
At Meta, we work to incorporate privacy-by-design into all of our products and keep user information secure. We have created an ML model that detects code changes ("diffs") that have privacy-sensitive implications. At our scale of tens of thousands of engineers creating hundreds of thousands of diffs each month, we use automated tools for detecting such diffs. Inspired by recent studies on detecting defects [2, 3, 5] and security vulnerabilities [4, 6, 7], we use techniques from natural language processing to build a deep learning system for detecting privacy-sensitive code.
References
[1]
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, NAACL-HLT 2019, Minneapolis, MN, USA, June 2--7, 2019, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 4171--4186.
[2]
Rafael Michael Karampatsis and Charles Sutton. 2020. SCELMo: Source Code Embeddings from Language Models. arXiv:2004.13214 [cs.SE]
[3]
Jian Li, Pinjia He, Jieming Zhu, and Michael R Lyu. 2017. Software defect prediction via convolutional neural network. In 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS). IEEE, 318--328.
[4]
Rebecca L. Russell, Louis Kim, Lei H. Hamilton, Tomo Lazovich, Jacob A. Harer, Onur Ozdemir, Paul M. Ellingwood, and Marc W. McConley. 2018. Automated Vulnerability Detection in Source Code Using Deep Representation Learning. arXiv:1807.04320 [cs.LG]
[5]
Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, and Rishabh Singh. 2019. Neural Program Repair by Jointly Learning to Localize and Repair. arXiv:1904.01720 [cs.LG]
[6]
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. Advances in neural information processing systems 32 (2019).
[7]
Noah Ziems and Shaoen Wu. 2021. Security Vulnerability Detection Using Deep Learning Natural Language Processing. arXiv:2105.02388 [cs.CR]
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Information
Published In
May 2022
815 pages
ISBN:9781450393034
DOI:10.1145/3524842
- General Chair:
- David Lo,
- Program Chairs:
- Shane McIntosh,
- Nicole Novielli
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Publication History
Published: 17 October 2022
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MSR '22
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MSR '22: 19th International Conference on Mining Software Repositories
May 23 - 24, 2022
Pennsylvania, Pittsburgh
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