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Pretrained code language models have enabled great progress towards program synthesis.
Roberta: A robustly optimized bert pretraining approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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Codesearchnet challenge: Evaluating the state of semantic code search
Husain, H.; Wu, H.-H.; Gazit, T.; Allamanis, M.; and Brockschmidt, M. 2019 · 1909
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Lewis, M.; Liu, Y.; Goyal, N.; Ghazvininejad, M.; Mohamed, A.; Levy, O.; Stoyanov, V.; and Zettlemoyer, L. 2019 · 1910
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Combining code embedding with static analysis for function-call completion
Weyssow, M.; Sahraoui, H.; Frénay, B.; and Vanderose, B. 2020 · 2008
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Mining Source Code Repositories at Massive Scale using Language Modeling
Allamanis, M.; and Sutton, C. 2013 · 2013
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On the naturalness of software
Hindle, A.; Barr, E. T.; Gabel, M.; Su, Z.; and Devanbu, P. 2016 · 2016
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Probabilistic Model for Code with Decision Trees
Raychev, V.; Bielik, P.; and Vechev, M. 2016 · 2016
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A survey of machine learning for big code and naturalness
Allamanis, M.; Barr, E. T.; Devanbu, P.; and Sutton, C. 2018 · 2018
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Code completion with neural attention and pointer networks
Li, J.; Wang, Y.; Lyu, M. R.; and King, I. 2018 · 2018
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Decoupled Weight Decay Regularization
Loshchilov, I.; and Hutter, F. 2018 · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
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Global Relational Models of Source Code
Hellendoorn, V. J.; Sutton, C.; Singh, R.; Maniatis, P.; and Bieber, D. 2019 · 2019
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Natural software revisited
Rahman, M.; Palani, D.; and Rigby, P. C. 2019 · 2019
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An empirical study on learning bug-fixing patches in the wild via neural machine translation
Tufano, M.; Watson, C.; Bavota, G.; Penta, M. D.; White, M.; and Poshyvanyk, D. 2019 · 2019
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Structural Language Models of Code
Alon, U.; Sadaka, R.; Levy, O.; and Yahav, E. 2020 · 2020
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Feng, Z.; Guo, D.; Tang, D.; Duan, N.; Feng, X.; Gong, M.; Shou, L.; Qin, B.; Liu, T.; Jiang, D.; et al. 2020 · 2020
Cited alongside, same era.
GraphCodeBERT: Pre-training Code Representations with Data Flow
Guo, D.; Ren, S.; Lu, S.; Feng, Z.; Tang, D.; Shujie, L.; Zhou, L.; Duan, N.; Svyatkovskiy, A.; Fu, S.; et al. 2020 · 2020
Cited alongside, same era.
Learning and evaluating contextual embedding of source code
Kanade, A.; Maniatis, P.; Balakrishnan, G.; and Shi, K. 2020 · 2020
Cited alongside, same era.
Big code!= big vocabulary: Open-vocabulary models for source code
Karampatsis, R.-M.; Babii, H.; Robbes, R.; Sutton, C.; and Janes, A. 2020 · 2020
Cited alongside, same era.
Multi-task learning based pre-trained language model for code completion
Liu, F.; Li, G.; Zhao, Y.; and Jin, Z. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Embedding API dependency graph for neural code generation
Lyu, C.; Wang, R.; Zhang, H.; Zhang, H.; and Hu, S. 2021 · 2021
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How could Neural Networks understand Programs?
Peng, D.; Zheng, S.; Li, Y.; Ke, G.; He, D.; and Liu, T.-Y. 2021 · 2021
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Project codenet: A large-scale ai for code dataset for learning a diversity of coding tasks
Puri, R.; Kung, D. S.; Janssen, G.; Zhang, W.; Domeniconi, G.; Zolotov, V.; Dolby, J.; Chen, J.; Choudhury, M.; Decker, L.; et al. 2021 · 2021
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Fast and memory-efficient neural code completion
Svyatkovskiy, A.; Lee, S.; Hadjitofi, A.; Riechert, M.; Franco, J. V.; and Allamanis, M. 2021 · 2021
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CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
Wang, Y.; Wang, W.; Joty, S.; and Hoi, S. C. 2021b · 2021
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Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; Liu, P. J.; et al. 2020 · 2020
Cited alongside, same era.
Intellicode compose: Code generation using transformer
Svyatkovskiy, A.; Deng, S. K.; Fu, S.; and Sundaresan, N. 2020 · 2020
Cited alongside, same era.
Unified Pre-training for Program Understanding and Generation
Ahmad, W.; Chakraborty, S.; Ray, B.; and Chang, K.-W. 2021 · 2021
Cited alongside, same era.
Program synthesis with large language models
Austin, J.; Odena, A.; Nye, M.; Bosma, M.; Michalewski, H.; Dohan, D.; Jiang, E.; Cai, C.; Terry, M.; Le, Q.; et al. 2021 · 2021
Cited alongside, same era.
Evaluating large language models trained on code
Chen, M.; Tworek, J.; Jun, H.; Yuan, Q.; Pinto, H. P. d. O.; Kaplan, J.; Edwards, H.; Burda, Y.; Joseph, N.; Brockman, G.; et al. 2021 · 2021
Cited alongside, same era.
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy
Clement, C.; Lu, S.; Liu, X.; Tufano, M.; Drain, D.; Duan, N.; Sundaresan, N.; and Svyatkovskiy, A. 2021 · 2021
Cited alongside, same era.
Measuring Coding Challenge Competence With APPS
Hendrycks, D.; Basart, S.; Kadavath, S.; Mazeika, M.; Arora, A.; Guo, E.; Burns, C.; Puranik, S.; He, H.; Song, D.; et al. 2021 · 2021
Cited alongside, same era.
Yasunaga, M.; and Liang, P. 2021 · 2021
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UniXcoder: Unified Cross-Modal Pre-training for Code Representation
Guo, D.; Lu, S.; Duan, N.; Wang, Y.; Zhou, M.; and Yin, J. 2022 · 2022
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FixEval: Execution-based Evaluation of Program Fixes for Competitive Programming Problems
Haque, M. M. A.; Ahmad, W. U.; Lourentzou, I.; and Brown, C. 2022 · 2022
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Competition-level code generation with alphacode
Li, Y.; Choi, D.; Chung, J.; Kushman, N.; Schrittwieser, J.; Leblond, R.; Eccles, T.; Keeling, J.; Gimeno, F.; Lago, A. D.; et al. 2022 · 2022
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Neural code completion
Liu, C.; Wang, X.; Shin, R.; Gonzalez, J. E.; and Song, D. 2016 · 2022
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López, J. A. H.; Weyssow, M.; Cuadrado, J. S.; and Sahraoui, H. 2022 · 2022
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ReACC: A Retrieval-Augmented Code Completion Framework
Lu, S.; Duan, N.; Han, H.; Guo, D.; Hwang, S.-w.; and Svyatkovskiy, A. 2022 · 2022
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A conversational paradigm for program synthesis
Nijkamp, E.; Pang, B.; Hayashi, H.; Tu, L.; Wang, H.; Zhou, Y.; Savarese, S.; and Xiong, C. 2022 · 2022
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Wang, B.; and Komatsuzaki, A. 2021 · 2022
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