Constrained Decoding for Secure Code Generation

M. Welling and Y. W. Teh, “Bayesian Learning via Stochastic Gradient Langevin dynamics,” in Proceedings of the 28th international conference on machine learning (ICML-11) . Citeseer, 2011, pp. 681–688

2011

Earlier work this paper cites.

P. Anderson, B. Fernando, M. Johnson, and S. Gould, “Guided Open Vocabulary Image Captioning with Constrained Beam Search,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , 2017, pp. 936–945

2017

Earlier work this paper cites.

C. D. V. Hoang, G. Haffari, and T. Cohn, “Towards Decoding as Continuous Optimisation in Neural Machine Translation,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , 2017, pp. 146–156

2017

Earlier work this paper cites.

M. Post and D. Vilar, “Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) , 2018, pp. 1314–1324

2018

Earlier work this paper cites.

A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi, “The Curious Case of Neural Text Degeneration,” in International Conference on Learning Representations , 2020

2020

Earlier work this paper cites.

GitHub, “Github Copilot: Your AI Pair Programmer,” https://github.com/features/copilot/ , 2021

2021

Earlier work this paper cites.

M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman et al. , “Evaluating large language models trained on code,” arXiv preprint arXiv:2107.03374 , 2021

Original

2021

Earlier work this paper cites.

J. Austin, A. Odena, M. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. Cai, M. Terry, Q. Le et al. , “Program Synthesis with Large Language Models,” arXiv preprint arXiv:2108.07732 , 2021

Original

2021

Earlier work this paper cites.

X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , 2021, pp. 4582–4597

2021

Earlier work this paper cites.

X. Lu, P. West, R. Zellers, R. Le Bras, C. Bhagavatula, and Y. Choi, “NeuroLogic Decoding:(Un) supervised Neural Text Generation with Predicate Logic Constraints,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2021, pp. 4288–4299

2021

Earlier work this paper cites.

S. Kumar, E. Malmi, A. Severyn, and Y. Tsvetkov, “Controlled Text Generation as Continuous Optimization with Multiple Constraints,” in Advances in Neural Information Processing Systems , 2021

2021

Earlier work this paper cites.

Eirini Kalliamvakou, GitHub Blog, “Research: quantifying GitHub Copilot’s impact on developer productivity and happiness,” https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/ , 2022

2022

Earlier work this paper cites.

Maxim Tabachnyk and Stoyan Nikolov, Google Research, “ML-Enhanced Code Completion Improves Developer Productivity,” https://research.google/blog/ml-enhanced-code-completion-improves-developer-productivity/ , 2022

2022

Earlier work this paper cites.

H. Pearce, B. Ahmad, B. Tan, B. Dolan-Gavitt, and R. Karri, “Asleep at the keyboard? assessing the security of github copilot’s code contributions,” in 2022 IEEE Symposium on Security and Privacy (SP) . IEEE, 2022, pp. 754–768

2022

Earlier work this paper cites.

M. L. Siddiq and J. C. Santos, “Securityeval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques,” in Proceedings of the 1st International Workshop on Mining Software Repositories Applications for Privacy and Security , 2022, pp. 29–33

2022

Earlier work this paper cites.

S. Kumar, B. Paria, and Y. Tsvetkov, “Gradient-based constrained sampling from language models,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , 2022

2022

Cited alongside, same era.

E. Nijkamp, B. Pang, H. Hayashi, L. Tu, H. Wang, Y. Zhou, S. Savarese, and C. Xiong, “Codegen: An open large language model for code with multi-turn program synthesis,” arXiv preprint arXiv:2203.13474 , 2022

Original

2022

Cited alongside, same era.

Chan Woo Kim, “Guiding Text Generation with Constrained Beam Search in Transformers,” https://huggingface.co/blog/constrained-beam-search , 2022

2022

Cited alongside, same era.

L. Qin, S. Welleck, D. Khashabi, and Y. Choi, “COLD decoding: Energy-based constrained text generation with langevin dynamics,” in Advances in Neural Information Processing Systems , 2022

2022

Cited alongside, same era.

H. Pearce, B. Tan, B. Ahmad, R. Karri, and B. Dolan-Gavitt, “Examining Zero-Shot Vulnerability Repair with Large Language Models,” in 2023 IEEE Symposium on Security and Privacy (SP) . IEEE, 2023, pp. 2339–2356

2023

Later among the works it cites.

A. Storhaug, J. Li, and T. Hu, “Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding,” in 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 2023, pp. 683–693

2023

Later among the works it cites.

H. Hajipour, K. Hassler, T. Holz, L. Schönherr, and M. Fritz, “CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models,” in 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) . IEEE, 2024, pp. 684–709

2024

Closest in time.

J. Liu, C. S. Xia, Y. Wang, and L. Zhang, “Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation,” Advances in Neural Information Processing Systems , vol. 36, 2024