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Semantic understanding of programs is a fundamental problem for programming language processing (PLP).
Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints
Cousot, P. and Cousot, R · 1977
Earlier work this paper cites.
Systematic design of program analysis frameworks
Cousot, P. and Cousot, R · 1979
Earlier work this paper cites.
A structural approach to operational semantics
Plotkin, G. D · 1981
Earlier work this paper cites.
The semantics of programming languages: an elementary introduction using structural operational semantics
Hennessy, M · 1990
Earlier work this paper cites.
Semantics of programming languages: structures and techniques
Gunter, C. A · 1992
Earlier work this paper cites.
The formal semantics of programming languages: an introduction
Winskel, G · 1993
Earlier work this paper cites.
Programl: Graph-based deep learning for program optimization and analysis
Cummins, C., Fisches, Z. V., Ben-Nun, T., Hoefler, T., and Leather, H · 2003
Earlier work this paper cites.
Graph-based comparison of executable objects (english version)
Dullien, T. and Rolles, R · 2005
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
Hadsell, R., Chopra, S., and LeCun, Y · 2006
Earlier work this paper cites.
Cummins, C., Leather, H., Fisches, Z., Ben-Nun, T., Hoefler, T., and O’Boyle, M · 2012
Earlier work this paper cites.
Revised report on the algorithmic language Algol 68
van Wijngaarden, A., Mailloux, B. J., Peck, J. E. L., Koster, C. H., Lindsey, C., Sintzoff, M., Meertens, L. G., and Fisker, R · 2012
Earlier work this paper cites.
Understanding Computation: From Simple Machines to Impossible Programs
Stuart, T · 2013
Earlier work this paper cites.
Convolutional neural networks over tree structures for programming language processing
Mou, L., Li, G., Zhang, L., Wang, T., and Jin, Z · 2016
Earlier work this paper cites.
Abstract syntax networks for code generation and semantic parsing
Rabinovich, M., Stern, M., and Klein, D · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code
Wei, H. and Li, M · 2017
Earlier work this paper cites.
A syntactic neural model for general-purpose code generation
Yin, P. and Neubig, G · 2017
Earlier work this paper cites.
Learning to represent programs with graphs
Allamanis, M., Brockschmidt, M., and Khademi, M · 2018
Earlier work this paper cites.
code2seq: Generating sequences from structured representations of code
Alon, U., Brody, S., Levy, O., and Yahav, E · 2018
Earlier work this paper cites.
Neural code comprehension: A learnable representation of code semantics
Ben-Nun, T., Jakobovits, A. S., and Hoefler, T · 2018
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Generative code modeling with graphs
Brockschmidt, M., Allamanis, M., Gaunt, A. L., and Polozov, O · 2018
Cited alongside, same era.
Tree-to-tree neural networks for program translation
Chen, X., Liu, C., and Song, D · 2018
Cited alongside, same era.
code2vec: Learning distributed representations of code
Alon, U., Zilberstein, M., Levy, O., and Yahav, E · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
Asm2vec: Boosting static representation robustness for binary clone search against code obfuscation and compiler optimization
Funnel-transformer: Filtering out sequential redundancy for efficient language processing
Dai, Z., Lai, G., Yang, Y., and Le, Q. V · 2020
Later among the works it cites.
Neural reverse engineering of stripped binaries using augmented control flow graphs
David, Y., Alon, U., and Yahav, E · 2020
Later among the works it cites.
Deepbindiff: Learning program-wide code representations for binary diffing
Duan, Y., Li, X., Wang, J., and Yin, H · 2020
Later among the works it cites.
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
Later among the works it cites.
Graphcodebert: Pre-training code representations with data flow
Guo, D., Ren, S., Lu, S., Feng, Z., Tang, D., Liu, S., Zhou, L., Duan, N., Yin, J., Jiang, D., et al · 2020
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Ding, S. H., Fung, B. C., and Charland, P · 2019
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Global relational models of source code
Hellendoorn, V. J., Sutton, C., Singh, R., Maniatis, P., and Bieber, D · 2019
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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
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Aroma: Code recommendation via structural code search
Luan, S., Yang, D., Barnaby, C., Sen, K., and Chandra, S · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N., Gurevych, I., Reimers, N., Gurevych, I., Thakur, N., Reimers, N., Daxenberger, J., and Gurevych, I · 2019
Cited alongside, same era.
A novel neural source code representation based on abstract syntax tree
Zhang, J., Wang, X., Zhang, H., Sun, H., Wang, K., and Liu, X · 2019
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Contrastive code representation learning
Jain, P., Jain, A., Zhang, T., Abbeel, P., Gonzalez, J. E., and Stoica, I · 2020
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Learning and evaluating contextual embedding of source code
Kanade, A., Maniatis, P., Balakrishnan, G., and Shi, K · 2020
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Rethinking the positional encoding in language pre-training
Ke, G., He, D., and Liu, T.-Y · 2020
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Code prediction by feeding trees to transformers
Kim, S., Zhao, J., Tian, Y., and Chandra, S · 2020
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Unsupervised translation of programming languages
Lachaux, M.-A., Roziere, B., Chanussot, L., and Lample, G · 2020
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Intellicode compose: Code generation using transformer
Svyatkovskiy, A., Deng, S. K., Fu, S., and Sundaresan, N · 2020
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Ir2vec: Llvm ir based scalable program embeddings
VenkataKeerthy, S., Aggarwal, R., Jain, S., Desarkar, M. S., Upadrasta, R., and Srikant, Y · 2020
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Blended, precise semantic program embeddings
Wang, K. and Su, Z · 2020
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Detecting code clones with graph neural network and flow-augmented abstract syntax tree
Wang, W., Li, G., Ma, B., Xia, X., and Jin, Z · 2020
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On layer normalization in the transformer architecture
Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., and Liu, T · 2020
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Misim: An end-to-end neural code similarity system
Ye, F., Zhou, S., Venkat, A., Marucs, R., Tatbul, N., Tithi, J. J., Petersen, P., Mattson, T., Kraska, T., Dubey, P., et al · 2020
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Unified pre-training for program understanding and generation
Ahmad, W. U., Chakraborty, S., Ray, B., and Chang, K.-W · 2021
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