Fetching the paper…
Reading the bibliography…
Language Models (LMs) have become widely used in software engineering, especially for tasks such as code generation, where they are referred to as code LMs.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Annual Meeting of the Association for Computational Linguistics , 2002
2002
Earlier work this paper cites.
C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Annual Meeting of the Association for Computational Linguistics , 2004
2004
Earlier work this paper cites.
J. Kirkpatrick, R. Pascanu, N. C. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell, “Overcoming catastrophic forgetting in neural networks,” Proceedings of the National Academy of Sciences , vol. 114, pp. 3521 – 3526, 2016. [Online]. Available: https://api.semanticscholar.org/CorpusID:4704285
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
P. Yin, B. Deng, E. Chen, B. Vasilescu, and G. Neubig, “Learning to mine aligned code and natural language pairs from stack overflow,” 2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR) , pp. 476–486, 2018
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Chattopadhyay, P. Manupriya, A. Sarkar, and V. N. Balasubramanian, “Neural network attributions: A causal perspective,” in International Conference on Machine Learning , 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:59606233
2019
Earlier work this paper cites.
J. Sohn, S. Kang, and S. Yoo, “Arachne: Search-based repair of deep neural networks,” ACM Transactions on Software Engineering and Methodology , vol. 32, pp. 1 – 26, 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:251623079
2019
Earlier work this paper cites.
T. H. M. Le, H. Chen, and M. A. Babar, “Deep learning for source code modeling and generation,” ACM Computing Surveys (CSUR) , vol. 53, pp. 1 – 38, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
R. Tufano, L. Pascarella, M. Tufano, D. Poshyvanyk, and G. Bavota, “Towards automating code review activities,” 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) , pp. 163–174, 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:230799433
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
X. Cai, J. Huang, Y.-L. Bian, and K. W. Church, “Isotropy in the contextual embedding space: Clusters and manifolds,” in International Conference on Learning Representations , 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:235614342
2021
Earlier work this paper cites.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
P. Liguori, E. Al-Hossami, D. Cotroneo, R. Natella, B. Cukic, and S. Shaikh, “Can we generate shellcodes via natural language? an empirical study,” Automated Software Engineering , vol. 29, 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:233407761
2021
Cited alongside, same era.
2021
Cited alongside, same era.
C. Xia, Y. Wei, and L. Zhang, “Automated program repair in the era of large pre-trained language models,” 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) , pp. 1482–1494, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:259860439
2023
Closest in time.
D. Jayasuriya, V. Terragni, J. Dietrich, S. Ou, and K. Blincoe, “Understanding breaking changes in the wild,” Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis , 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:259844850
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…
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
J. Gu, P. Salza, and H. C. Gall, “Assemble foundation models for automatic code summarization,” 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) , pp. 935–946, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
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,” 2022
2022
Cited alongside, same era.
X. Gao, J. Zhai, S. Ma, C. Shen, Y. Chen, and Q. Wang, “Fairneuron: Improving deep neural network fairness with adversary games on selective neurons,” 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) , pp. 921–933, 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:247996805
2022
Cited alongside, same era.
2023
Closest in time.
2023
Closest in time.
D. L. Calsi, M. Duran, T. Laurent, X. Zhang, P. Arcaini, and F. Ishikawa, “Adaptive search-based repair of deep neural networks,” Proceedings of the Genetic and Evolutionary Computation Conference , 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:259833669
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2024
Closest in time.
2024
Closest in time.
Transluce Organization, “Observability interface,” 2024. [Online]. Available: https://transluce.org/observability-interface
2024
Closest in time.
2024
Closest in time.
J. Gu, A. Aleti, C. Chen, and H. Zhang, “Vocabulary-defined semantics: Latent space clustering for improving in-context learning,” 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:267312600
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
J. Ma, P. Yang, J. Wang, Y. Sun, C.-C. Huang, and Z. Wang, “Vere: Verification guided synthesis for repairing deep neural networks,” 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE) , pp. 64–76, 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:267388361
2024
Closest in time.
2024
Closest in time.
J. Chen, Z. Pan, X. Hu, Z. Li, G. Li, and X. Xia, “Reasoning runtime behavior of a program with llm: How far are we?” 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:268680421
2024
Closest in time.