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Pre-trained programming language (PL) models (such as CodeT5, CodeBERT, GraphCodeBERT, etc.,) have the potential to automate software engineering tasks involving code understanding and code generation.
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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Equation of state calculations by fast computing machines
Metropolis, N.; Rosenbluth, A. W.; Rosenbluth, M. N.; Teller, A. H.; and Teller, E. 1953 · 1953
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Bleu: a Method for Automatic Evaluation of Machine Translation
Papineni, K.; Roukos, S.; Ward, T.; and Zhu, W.-J. 2002 · 2002
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Codebleu: a method for automatic evaluation of code synthesis
Ren, S.; Guo, D.; Lu, S.; Zhou, L.; Liu, S.; Tang, D.; Sundaresan, N.; Zhou, M.; Blanco, A.; and Ma, S. 2020 · 2009
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Improving automated source code summarization via an eye-tracking study of programmers
Rodeghero, P.; McMillan, C.; McBurney, P. W.; Bosch, N.; and D’Mello, S. 2014 · 2014
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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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Technical report on the cleverhans v2. 1.0 adversarial examples library
Papernot, N.; Faghri, F.; Carlini, N.; Goodfellow, I.; Feinman, R.; Kurakin, A.; Xie, C.; Sharma, Y.; Brown, T.; Roy, A.; et al. 2016 · 2016
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P.; Zhang, J.; Lopyrev, K.; and Liang, P. 2016 · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Wu, Y.; Schuster, M.; Chen, Z.; Le, Q. V.; Norouzi, M.; Macherey, W.; Krikun, M.; Cao, Y.; Gao, Q.; Macherey, K.; et al. 2016 · 2016
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RACE: Large-scale ReAding Comprehension Dataset From Examinations
Lai, G.; Xie, Q.; Liu, H.; Yang, Y.; and Hovy, E. 2017 · 2017
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Generating Natural Language Adversarial Examples
Alzantot, M.; Sharma, Y.; Elgohary, A.; Ho, B.-J.; Srivastava, M.; and Chang, K.-W. 2018 · 2018
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HotFlip: White-Box Adversarial Examples for Text Classification
Ebrahimi, J.; Rao, A.; Lowd, D.; and Dou, D. 2018 · 2018
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Black-box generation of adversarial text sequences to evade deep learning classifiers
Gao, J.; Lanchantin, J.; Soffa, M. L.; and Qi, Y. 2018 · 2018
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GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Wang, A.; Singh, A.; Michael, J.; Hill, F.; Levy, O.; and Bowman, S. 2018 · 2018
Cited alongside, same era.
On the robustness of self-attentive models
Hsieh, Y.-L.; Cheng, M.; Juan, D.-C.; Wei, W.; Hsu, W.-L.; and Hsieh, C.-J. 2019 · 2019
Cited alongside, same era.
TextBugger: Generating Adversarial Text Against Real-world Applications
Li, J.; Ji, S.; Du, T.; Li, B.; and Wang, T. 2019 · 2019
Cited alongside, same era.
Combating Adversarial Misspellings with Robust Word Recognition
Pruthi, D.; Dhingra, B.; and Lipton, Z. C. 2019 · 2019
Cited alongside, same era.
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency
Ren, S.; Deng, Y.; He, K.; and Che, W. 2019 · 2019
Cited alongside, same era.
An empirical study on learning bug-fixing patches in the wild via neural machine translation
Generating adversarial examples for holding robustness of source code processing models
Zhang, H.; Li, Z.; Li, G.; Ma, L.; Liu, Y.; and Jin, Z. 2020 · 2020
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Unified Pre-training for Program Understanding and Generation
Ahmad, W.; Chakraborty, S.; Ray, B.; and Chang, K.-W. 2021 · 2021
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Assessing Robustness of ML-Based Program Analysis Tools using Metamorphic Program Transformations
Applis, L.; Panichella, A.; and van Deursen, A. 2021 · 2021
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DOBF: A Deobfuscation Pre-Training Objective for Programming Languages
Lachaux, M.-A.; Roziere, B.; Szafraniec, M.; and Lample, G. 2021 · 2021
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CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Lu, S.; Guo, D.; Ren, S.; Huang, J.; Svyatkovskiy, A.; Blanco, A.; Clement, C. B.; Drain, D.; Jiang, D.; Tang, D.; Li, G.; Zhou, L.; Shou, L.; Zhou, L.; Tufano, M.; Gong, M.; Zhou, M.; Duan, N.; Sundaresan, N.; Deng, S. K.; Fu, S.; and Liu, S. 2021 · 2021
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Tufano, M.; Watson, C.; Bavota, G.; Penta, M. D.; White, M.; and Poshyvanyk, D. 2019 · 2019
Cited alongside, same era.
A theory of dual channel constraints
Casalnuovo, C.; Barr, E. T.; Dash, S. K.; Devanbu, P.; and Morgan, E. 2020 · 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.; et al. 2020 · 2020
Cited alongside, same era.
BAE: BERT-based Adversarial Examples for Text Classification
Garg, S.; and Ramakrishnan, G. 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.
Is bert really robust? a strong baseline for natural language attack on text classification and entailment
Jin, D.; Jin, Z.; Zhou, J. T.; and Szolovits, P. 2020 · 2020
Cited alongside, same era.
BERT-ATTACK: Adversarial Attack Against BERT Using BERT
Li, L.; Ma, R.; Guo, Q.; Xue, X.; and Qiu, X. 2020 · 2020
Cited alongside, same era.
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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
Later among the works it cites.
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
Wang, Y.; Wang, W.; Joty, S.; and Hoi, S. C. 2021 · 2021
Later among the works it cites.
NatGen: generative pre-training by “naturalizing” source code
Chakraborty, S.; Ahmed, T.; Ding, Y.; Devanbu, P. T.; and Ray, B. 2022 · 2022
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Semantic Robustness of Models of Source Code
Henkel, J.; Ramakrishnan, G.; Wang, Z.; Albarghouthi, A.; Jha, S.; and Reps, T. 2022 · 2022
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StructCoder: Structure-Aware Transformer for Code Generation
Tipirneni, S.; Zhu, M.; and Reddy, C. K. 2022 · 2022
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Natural Attack for Pre-Trained Models of Code
Yang, Z.; Shi, J.; He, J.; and Lo, D. 2022 · 2022
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Diet code is healthy: Simplifying programs for pre-trained models of code
Zhang, Z.; Zhang, H.; Shen, B.; and Gu, X. 2022 · 2022
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Adversarial robustness of deep code comment generation
Zhou, Y.; Zhang, X.; Shen, J.; Han, T.; Chen, T.; and Gall, H. 2022 · 2022
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