Fetching the paper…
Reading the bibliography…
Python's dynamic typing system offers flexibility and expressiveness but can lead to type-related errors, prompting the need for automated type inference to enhance type hinting.
C. Anderson, P. Giannini, and S. Drossopoulou, “Towards type inference for javascript,” in ECOOP 2005 - Object-Oriented Programming, 19th European Conference, Glasgow, UK, July 25-29, 2005, Proceedings , ser. Lecture Notes in Computer Science, A. P. Black, Ed., vol. 3586. Springer, 2005, pp. 428–452
2005
Earlier work this paper cites.
S. H. Jensen, A. Møller, and P. Thiemann, “Type analysis for javascript,” in Static Analysis, 16th International Symposium, SAS 2009, Los Angeles, CA, USA, August 9-11, 2009. Proceedings , ser. Lecture Notes in Computer Science, J. Palsberg and Z. Su, Eds., vol. 5673. Springer, 2009, pp. 238–255
2009
Earlier work this paper cites.
S. Bengio, O. Vinyals, N. Jaitly, and N. Shazeer, “Scheduled sampling for sequence prediction with recurrent neural networks,” in Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada , C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, Eds., 2015, pp. 1171–1179
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , Y. Bengio and Y. LeCun, Eds., 2015
2015
Earlier work this paper cites.
S. Chen and M. Erwig, “Principal type inference for gadts,” in Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL 2016, St. Petersburg, FL, USA, January 20 - 22, 2016 , R. Bodík and R. Majumdar, Eds. ACM, 2016, pp. 416–428
2016
Earlier work this paper cites.
M. Emmi and C. Enea, “Symbolic abstract data type inference,” in Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL 2016, St. Petersburg, FL, USA, January 20 - 22, 2016 , R. Bodík and R. Majumdar, Eds. ACM, 2016, pp. 513–525
2016
Earlier work this paper cites.
Z. Xu, X. Zhang, L. Chen, K. Pei, and B. Xu, “Python probabilistic type inference with natural language support,” in Proceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2016, Seattle, WA, USA, November 13-18, 2016 , T. Zimmermann, J. Cleland-Huang, and Z. Su, Eds. ACM, 2016, pp. 607–618
2016
Earlier work this paper cites.
A. van den Oord, O. Vinyals, and K. Kavukcuoglu, “Neural discrete representation learning,” in Proceedings of Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, Eds., 2017, pp. 6306–6315
2017
Earlier work this paper cites.
J. Ore, S. G. Elbaum, C. Detweiler, and L. Karkazis, “Assessing the type annotation burden,” in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, ASE 2018, Montpellier, France, September 3-7, 2018 , M. Huchard, C. Kästner, and G. Fraser, Eds. ACM, 2018, pp. 190–201
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Fan, M. Lewis, and Y. N. Dauphin, “Hierarchical neural story generation,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers , I. Gurevych and Y. Miyao, Eds. Association for Computational Linguistics, 2018, pp. 889–898
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
M. Allamanis, E. T. Barr, S. Ducousso, and Z. Gao, “Typilus: neural type hints,” in Proceedings of the 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation, PLDI 2020, London, UK, June 15-20, 2020 , A. F. Donaldson and E. Torlak, Eds. ACM, 2020, pp. 91–105
2020
Earlier work this paper cites.
M. Pradel, G. Gousios, J. Liu, and S. Chandra, “Typewriter: neural type prediction with search-based validation,” in Proceedings of 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Virtual Event, USA, November 8-13, 2020 , P. Devanbu, M. B. Cohen, and T. Zimmermann, Eds. ACM, 2020, pp. 209–220
2020
Earlier work this paper cites.
Z. Feng, D. Guo, D. Tang, N. Duan, X. Feng, M. Gong, L. Shou, B. Qin, T. Liu, D. Jiang, and M. Zhou, “Codebert: A pre-trained model for programming and natural languages,” in Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020 , ser. Findings of ACL, T. Cohn, Y. He, and Y. Liu, Eds., vol. EMNLP 2020. Association for Computational Linguistics, 2020, pp. 1536–1547
2020
Earlier work this paper cites.
T. Chen, S. Kornblith, M. Norouzi, and G. E. Hinton, “A simple framework for contrastive learning of visual representations,” in Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event , ser. Proceedings of Machine Learning Research, vol. 119. PMLR, 2020, pp. 1597–1607
2020
Earlier work this paper cites.
K. He, H. Fan, Y. Wu, S. Xie, and R. B. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020 . Computer Vision Foundation / IEEE, 2020, pp. 9726–9735
2020
Earlier work this paper cites.
J. Wei, M. Goyal, G. Durrett, and I. Dillig, “Lambdanet: Probabilistic type inference using graph neural networks,” in Proceedings of 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020
2020
Earlier work this paper cites.
A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi, “The curious case of neural text degeneration,” in 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” J. Mach. Learn. Res. , vol. 21, pp. 140:1–140:67, 2020
2020
Earlier work this paper cites.
K. Jesse, P. T. Devanbu, and T. Ahmed, “Learning type annotation: is big data enough?” in Proceedings of 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Athens, Greece, August 23-28, 2021 , D. Spinellis, G. Gousios, M. Chechik, and M. D. Penta, Eds. ACM, 2021, pp. 1483–1486
2021
Earlier work this paper cites.
Y. Yan, Y. Feng, H. Fan, and B. Xu, “Dlinfer: Deep learning with static slicing for python type inference,” in 45th IEEE/ACM International Conference on Software Engineering, ICSE 2023, Melbourne, Australia, May 14-20, 2023 . IEEE, 2023, pp. 2009–2021
2021
Cited alongside, same era.
Y. Wang, W. Wang, S. R. Joty, and S. C. H. Hoi, “Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021 , M. Moens, X. Huang, L. Specia, and S. W. Yih, Eds. Association for Computational Linguistics, 2021, pp. 8696–8708
2021
Cited alongside, same era.
A. M. Mir, E. Latoskinas, and G. Gousios, “Manytypes4py: A benchmark python dataset for machine learning-based type inference,” in Proceedings of 18th IEEE/ACM International Conference on Mining Software Repositories, MSR 2021, Madrid, Spain, May 17-19, 2021 . IEEE, 2021, pp. 585–589
2021
Cited alongside, same era.
D. Fried, A. Aghajanyan, J. Lin, S. Wang, E. Wallace, F. Shi, R. Zhong, S. Yih, L. Zettlemoyer, and M. Lewis, “Incoder: A generative model for code infilling and synthesis,” in Proceedings of The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net, 2023
2023
Later among the works it cites.
J. D. Zamfirescu-Pereira, R. Y. Wong, B. Hartmann, and Q. Yang, “Why johnny can’t prompt: How non-ai experts try (and fail) to design LLM prompts,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023, Hamburg, Germany, April 23-28, 2023 , A. Schmidt, K. Väänänen, T. Goyal, P. O. Kristensson, A. Peters, S. Mueller, J. R. Williamson, and M. L. Wilson, Eds. ACM, 2023, pp. 437:1–437:21
2023
Later among the works it cites.
(2023) Openai api reference. [Online]. Available: https://platform.openai.com/docs/api-reference/
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Z. Pavlinovic, Y. Su, and T. Wies, “Data flow refinement type inference,” Proc. ACM Program. Lang. , vol. 5, no. POPL, pp. 1–31, 2021
2021
Cited alongside, same era.
A. Karmakar and R. Robbes, “What do pre-trained code models know about code?” in 36th IEEE/ACM International Conference on Automated Software Engineering, ASE 2021, Melbourne, Australia, November 15-19, 2021 . IEEE, 2021, pp. 1332–1336
2021
Cited alongside, same era.
D. Guo, S. Ren, S. Lu, Z. Feng, D. Tang, S. Liu, L. Zhou, N. Duan, A. Svyatkovskiy, S. Fu, M. Tufano, S. K. Deng, C. B. Clement, D. Drain, N. Sundaresan, J. Yin, D. Jiang, and M. Zhou, “Graphcodebert: Pre-training code representations with data flow,” in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 . OpenReview.net, 2021
2021
Cited alongside, same era.
A. M. Mir, E. Latoskinas, S. Proksch, and G. Gousios, “Type4py: Practical deep similarity learning-based type inference for python,” in Proceedings of 44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022 . ACM, 2022, pp. 2241–2252
2022
Cited alongside, same era.
D. Guo, S. Lu, N. Duan, Y. Wang, M. Zhou, and J. Yin, “Unixcoder: Unified cross-modal pre-training for code representation,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022 , S. Muresan, P. Nakov, and A. Villavicencio, Eds. Association for Computational Linguistics, 2022, pp. 7212–7225
2022
Cited alongside, same era.
C. An, J. Feng, K. Lv, L. Kong, X. Qiu, and X. Huang, “Cont: Contrastive neural text generation,” in NeurIPS , 2022
2022
Cited alongside, same era.
Y. Wan, W. Zhao, H. Zhang, Y. Sui, G. Xu, and H. Jin, “What do they capture? - A structural analysis of pre-trained language models for source code,” in 44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022 . ACM, 2022, pp. 2377–2388
2022
Cited alongside, same era.
C. Wang, Y. Yang, C. Gao, Y. Peng, H. Zhang, and M. R. Lyu, “No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2022, Singapore, Singapore, November 14-18, 2022 , A. Roychoudhury, C. Cadar, and M. Kim, Eds. ACM, 2022, pp. 382–394
2022
Cited alongside, same era.
H. Ye, W. Chen, W. Dou, G. Wu, and J. Wei, “Knowledge-based environment dependency inference for python programs,” in 44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022 . ACM, 2022, pp. 1245–1256
2022
Cited alongside, same era.
X. Wang, J. Wei, D. Schuurmans, Q. V. Le, E. H. Chi, S. Narang, A. Chowdhery, and D. Zhou, “Self-consistency improves chain of thought reasoning in language models,” in Proceedings of The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net, 2023
2023
Later among the works it cites.
J. Wei, G. Durrett, and I. Dillig, “Typet5: Seq2seq type inference using static analysis,” in Proceedings of The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
E. Shi, Y. Wang, W. Gu, L. Du, H. Zhang, S. Han, D. Zhang, and H. Sun, “Cocosoda: Effective contrastive learning for code search,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 2198–2210
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Pearce, B. Tan, B. Ahmad, R. Karri, and B. Dolan-Gavitt, “Examining zero-shot vulnerability repair with large language models,” in 44th IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, May 21-25, 2023 . IEEE, 2023, pp. 2339–2356
2023
Later among the works it cites.
K. Huang, X. Meng, J. Zhang, Y. Liu, W. Wang, S. Li, and Y. Zhang, “An empirical study on fine-tuning large language models of code for automated program repair,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 1162–1174
2023
Later among the works it cites.
C. Wang, Y. Lou, J. Liu, and X. Peng, “Generating variable explanations via zero-shot prompt learning,” in 38th IEEE/ACM International Conference on Automated Software Engineering, ASE 2023, Luxembourg, September 11-15, 2023 . IEEE, 2023, pp. 748–760
2023
Later among the works it cites.
(2023) Transformers. [Online]. Available: https://github.com/huggingface/transformers
2023
Later among the works it cites.
(2023) Hugging face – codet5-base. [Online]. Available: https://huggingface.co/Salesforce/codet5-base
2023
Later among the works it cites.
(2023) Hugging face – codet5-base. [Online]. Available: https://github.com/JohnnyPeng18/TypeGen/releases/tag/data
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
X. Du, M. Liu, K. Wang, H. Wang, J. Liu, Y. Chen, J. Feng, C. Sha, X. Peng, and Y. Lou, “Evaluating large language models in class-level code generation,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
2024
Closest in time.
Y. Peng, C. Gao, Z. Li, B. Gao, D. Lo, Q. Zhang, and M. R. Lyu, “Static inference meets deep learning: A hybrid type inference approach for python,” in Proceedings of 44th IEEE/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25-27, 2022 . ACM, 2022, pp. 2019–2030
2030
Closest in time.