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Language models of code (LMs) work well when the surrounding code provides sufficient context.
Incremental context-dependent analysis for language-based editors
Reps, T., Teitelbaum, T., and Demers, A · 1983
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Typestate: A programming language concept for enhancing software reliability
Strom, R. E. and Yemini, S · 1986
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Incremental semantic analysis
Hedin, G · 1992
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Incremental static semantic analysis
Maddox III, W. H · 1997
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Practical algorithms for incremental software development environments
Wagner, T. A · 1997
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Enabling static analysis for partial java programs
Dagenais, B. and Hendren, L · 2008
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Japanese and korean voice search
Schuster, M. and Nakajima, K · 2012
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Leveraging static analysis in an ide
Fuhrer, R. M · 2013
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Session types for rust
Jespersen, T. B. L., Munksgaard, P., and Larsen, K. F · 2015
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Principles of program analysis
Nielson, F., Nielson, H. R., and Hankin, C · 2015
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URL https://projects.eclipse.org/projects/eclipse.jdt.ls
Eclipse JDT LS, September 2016 · 2016
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Asynchrony-aware static analysis of android applications
Mishra, A., Kanade, A., and Srikant, Y. N · 2016
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Neural Machine Translation of Rare Words with Subword Units
Sennrich, R., Haddow, B., and Birch, A · 2016
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Are deep neural networks the best choice for modeling source code?
Hellendoorn, V. J. and Devanbu, P · 2017
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URL https://rust-analyzer.github.io/
rust-analyzer, 2018 · 2018
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Kudo, T. and Richardson, J · 2018
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The embedded rust book, 2018
Rust on Embedded Devices Working Group et al · 2018
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Generative Code Modeling with Graphs
Brockschmidt, M., Allamanis, M., Gaunt, A. L., and Polozov, O · 2019
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Session types, November 2019
Crichton, W., Navarro, J., Semeniuta, D., Zeng, S., and Gupta, S · 2019
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Generalization through memorization: Nearest neighbor language models
Khandelwal, U., Levy, O., Jurafsky, D., Zettlemoyer, L., and Lewis, M · 2019
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jedi-language-server, 2019
Roeca, S · 2019
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URL https://clangd.llvm.org/
What is clangd?, 2020 · 2020
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Enabling language models to fill in the blanks
Donahue, C., Lee, M., and Liang, P · 2020
Cited alongside, same era.
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., and others · 2020
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Graphcodebert: Pre-training code representations with data flow
Guo, D., Ren, S., Lu, S., Feng, Z., Tang, D., Liu, S., Zhou, L., Duan, N., Svyatkovskiy, A., Fu, S., and others · 2020
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The Curious Case of Neural Text Degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y · 2020
Cited alongside, same era.
Incoder: A generative model for code infilling and synthesis
Fried, D., Aghajanyan, A., Lin, J., Wang, S., Wallace, E., Shi, F., Zhong, R., Yih, W.-t., Zettlemoyer, L., and Lewis, M · 2022
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Unixcoder: Unified cross-modal pre-training for code representation, 2022
Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., and Yin, J · 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., Hubert, T., Choy, P., d’Autume, C. d. M., Babuschkin, I., Chen, X., Huang, P.-S., Welbl, J., Gowal, S., Cherepanov, A., Molloy, J., Mankowitz, D. J., Robson, E. S., Kohli, P., Freitas, N. d., Kavukcuoglu, K., and Vinyals, O · 2022
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Training language models to follow instructions with human feedback, 2022
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R · 2022
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Kanade, A., Maniatis, P., Balakrishnan, G., and Shi, K · 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
Cited alongside, same era.
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le Scao, T., Gugger, S., Drame, M., Lhoest, Q., and Rush, A · 2020
Cited alongside, same era.
Unified pre-training for program understanding and generation, 2021
Ahmad, W. U., Chakraborty, S., Ray, B., and Chang, K.-W · 2021
Cited alongside, same era.
Program Synthesis with Large Language Models, August 2021
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., and Sutton, C · 2021
Cited alongside, same era.
Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow
Black, S., Gao, L., Wang, P., Leahy, C., and Biderman, S · 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., and others · 2021
Cited alongside, same era.
Pashakhanloo, P., Naik, A., Wang, Y., Dai, H., Maniatis, P., and Naik, M · 2022
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Synchromesh: Reliable code generation from pre-trained language models
Poesia, G., Polozov, A., Le, V., Tiwari, A., Soares, G., Meek, C., and Gulwani, S · 2022
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Repository-level prompt generation for large language models of code
Shrivastava, D., Larochelle, H., and Tarlow, D · 2022
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Natural language processing with transformers
Tunstall, L., Von Werra, L., and Wolf, T · 2022
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A Systematic Evaluation of Large Language Models of Code
Xu, F. F., Alon, U., Neubig, G., and Hellendoorn, V. J · 2022
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When Language Model Meets Private Library
Zan, D., Chen, B., Lin, Z., Guan, B., Yongji, W., and Lou, J.-G · 2022
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Docprompting: Generating code by retrieving the docs
Zhou, S., Alon, U., Xu, F. F., Jiang, Z., and Neubig, G · 2022
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https://protobuf.dev/ , 2008
Protocol Buffers · 2023
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https://projectlombok.org/ , 2009
Project Lombok · 2023
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https://microsoft.github.io/language-server-protocol/implementors/servers/ , 2023
Language Server Implementations · 2023
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SantaCoder: don’t reach for the stars!
Allal, L. B., Li, R., Kocetkov, D., Mou, C., Akiki, C., Ferrandis, C. M., Muennighoff, N., Mishra, M., Gu, A., Dey, M., and others · 2023
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Typed design patterns for the functional era
Crichton, W · 2023
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Starcoder: may the source be with you!
Li, R., Allal, L. B., Zi, Y., Muennighoff, N., Kocetkov, D., Mou, C., Marone, M., Akiki, C., Li, J., Chim, J., et al · 2023
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CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., Savarese, S., and Xiong, C · 2023
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Better context makes better code language models: A case study on function call argument completion
Pei, H., Zhao, J., Lausen, L., Zha, S., and Karypis, G · 2023
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Yu, H., Shen, B., Ran, D., Zhang, J., Zhang, Q., Ma, Y., Liang, G., Li, Y., Xie, T., and Wang, Q · 2023
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RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
Zhang, F., Chen, B., Zhang, Y., Liu, J., Zan, D., Mao, Y., Lou, J.-G., and Chen, W · 2023
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