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Large Language Models (LLMs) have significantly aided developers by generating or assisting in code writing, enhancing productivity across various tasks.
Z. Sun, Q. Zhu, Y. Xiong, Y. Sun, L. Mou, and L. Zhang, “Treegen: A tree-based transformer architecture for code generation,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 05, 2020, pp. 8984–8991
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A. Svyatkovskiy, S. Lee, A. Hadjitofi, M. Riechert, J. V. Franco, and M. Allamanis, “Fast and memory-efficient neural code completion,” in 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) . IEEE, 2021, pp. 329–340
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S. Kim, J. Zhao, Y. Tian, and S. Chandra, “Code prediction by feeding trees to transformers,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 2021, pp. 150–162
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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
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2022
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Y. Gao and C. Lyu, “M2ts: Multi-scale multi-modal approach based on transformer for source code summarization,” in Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension , 2022, pp. 24–35
2022
Cited alongside, same era.
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
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M. L. Siddiq, S. H. Majumder, M. R. Mim, S. Jajodia, and J. C. Santos, “An empirical study of code smells in transformer-based code generation techniques,” in 2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM) . IEEE, 2022, pp. 71–82
2022
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in neural information processing systems , vol. 35, pp. 27 730–27 744, 2022
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
2024
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2024
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2024
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2024
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2022
Cited alongside, same era.
C. S. Xia, Y. Wei, and L. Zhang, “Automated program repair in the era of large pre-trained language models,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 1482–1494
2023
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
J. He and M. Vechev, “Large language models for code: Security hardening and adversarial testing,” in Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security , 2023, pp. 1865–1879
2023
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2024
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H. Su, J. Niu, X. Liu, and M. Atiquzzaman, “Safecoder: A machine-learning-based encoding system to embed safety identification information into qr codes,” Journal of Network and Computer Applications , vol. 227, p. 103874, 2024
2024
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“Codeql documentation,” https://codeql.github.com/docs/
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