Fetching the paper…
Reading the bibliography…
Code generation is to automatically generate source code conforming to a given programming specification, which has received extensive attention especially with the development of large language models (LLMs).
F. Wilcoxon, S. Katti, R. A. Wilcox et al. , “Critical values and probability levels for the wilcoxon rank sum test and the wilcoxon signed rank test,” Selected tables in mathematical statistics , vol. 1, pp. 171–259, 1970
1970
Earlier work this paper cites.
K. Beck, Test-driven development: by example . Addison-Wesley Professional, 2003
2003
Earlier work this paper cites.
S. Gulwani, “Programming by examples: Applications, algorithms, and ambiguity resolution,” in Automated Reasoning: 8th International Joint Conference, IJCAR 2016, Coimbra, Portugal, June 27–July 2, 2016, Proceedings 8 . Springer, 2016, pp. 9–14
2016
Earlier work this paper cites.
2019
Earlier work this paper cites.
S. Kulal, P. Pasupat, K. Chandra, M. Lee, O. Padon, A. Aiken, and P. S. Liang, “Spoc: Search-based pseudocode to code,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
K. Gupta, P. E. Christensen, X. Chen, and D. Song, “Synthesize, execute and debug: Learning to repair for neural program synthesis,” Advances in Neural Information Processing Systems , vol. 33, pp. 17 685–17 695, 2020
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
OpenAI, “Chatgpt: Optimizing language models for dialogue.” https://openai.com/blog/chatgpt , 2022
2022
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in Neural Information Processing Systems , vol. 35, pp. 24 824–24 837, 2022
2022
Earlier work this paper cites.
B. Chen, F. Zhang, A. Nguyen, D. Zan, Z. Lin, J.-G. Lou, and W. Chen, “Codet: Code generation with generated tests,” in The Eleventh International Conference on Learning Representations , 2022
2022
Earlier work this paper cites.
T. Ahmed and P. Devanbu, “Few-shot training llms for project-specific code-summarization,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–5
2022
Earlier work this paper cites.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” Advances in neural information processing systems , vol. 35, pp. 22 199–22 213, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
Z. Tian, J. Chen, Q. Zhu, J. Yang, and L. Zhang, “Learning to construct better mutation faults,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–13
2022
Earlier work this paper cites.
A. Eghbali and M. Pradel, “Crystalbleu: precisely and efficiently measuring the similarity of code,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–12
2022
Earlier work this paper cites.
H. Le, Y. Wang, A. D. Gotmare, S. Savarese, and S. C. H. Hoi, “Coderl: Mastering code generation through pretrained models and deep reinforcement learning,” Advances in Neural Information Processing Systems , vol. 35, pp. 21 314–21 328, 2022
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
Z. Tian, J. Chen, and Z. Jin, “Code difference guided adversarial example generation for deep code models,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 850–862
2023
Cited alongside, same era.
Z. Tian, J. Chen, and X. Zhang, “On-the-fly improving performance of deep code models via input denoising,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 560–572
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Closest in time.
M. Liu, N. Pinckney, B. Khailany, and H. Ren, “Verilogeval: Evaluating large language models for verilog code generation,” in 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD) . IEEE, 2023, pp. 1–8
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
N. Nashid, M. Sintaha, and A. Mesbah, “Retrieval-based prompt selection for code-related few-shot learning,” in Proceedings of the 45th International Conference on Software Engineering (ICSE’23) , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
D. AI, “Deepseek coder: Let the code write itself.” https://github.com/deepseek-ai/DeepSeek-Coder , 2024
2024
Closest in time.
Z. Tian, H. Shu, D. Wang, X. Cao, Y. Kamei, and J. Chen, “Large language models for equivalent mutant detection: How far are we?” in Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis , 2024, pp. 1733–1745
2024
Closest in time.
2024
Closest in time.
T. Ahmed, K. S. Pai, P. Devanbu, and E. T. Barr, “Automatic semantic augmentation of language model prompts (for code summarization),” in 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE) . IEEE Computer Society, 2024, pp. 1004–1004
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
EvalPlus, “Evalplus leaderboard,” https://evalplus.github.io/leaderboard.html , 2024
2024
Closest in time.
T. Team, “Coding llms leaderboard,” https://leaderboard.tabbyml.com/ , 2024
2024
Closest in time.
W. Wang, C. Yang, Z. Wang, Y. Huang, Z. Chu, D. Song, L. Zhang, A. R. Chen, and L. Ma, “Testeval: Benchmarking large language models for test case generation,” https://llm4softwaretesting.github.io/ , 2024
2024
Closest in time.
EvalPlus, “Code sanitizer,” https://github.com/evalplus/evalplus?tab=readme-ov-file#code-post-processing , 2024
2024
Closest in time.
2024
Closest in time.
μ \mu FiX, https://github.com/tianzhaotju/muFiX , 2024
2024
Closest in time.