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Deep learning-based code processing models have shown good performance for tasks such as predicting method names, summarizing programs, and comment generation.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics , 2002, pp. 311–318
2002
Earlier work this paper cites.
S. Eder, M. Junker, E. Jürgens, B. Hauptmann, R. Vaas, and K.-H. Prommer, “How much does unused code matter for maintenance?” in 2012 34th International Conference on Software Engineering (ICSE) . IEEE, 2012, pp. 1102–1111
2012
Earlier work this paper cites.
M. Zalewski, “American fuzzy lop,” 2014
2014
Earlier work this paper cites.
A. Rebert, S. K. Cha, T. Avgerinos, J. Foote, D. Warren, G. Grieco, and D. Brumley, “Optimizing seed selection for fuzzing,” in 23rd { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 14) , 2014, pp. 861–875
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
W. Yin, H. Schütze, B. Xiang, and B. Zhou, “Abcnn: Attention-based convolutional neural network for modeling sentence pairs,” Transactions of the Association for Computational Linguistics , vol. 4, pp. 259–272, 2016
2016
Earlier work this paper cites.
M. Aizatsky, K. Serebryany, O. Chang, A. Arya, and M. Whittaker, “Announcing oss-fuzz: Continuous fuzzing for open source software,” Google Testing Blog , 2016
2016
Earlier work this paper cites.
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in 2016 IEEE European symposium on security and privacy (EuroS&P) . IEEE, 2016, pp. 372–387
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. Pei, Y. Cao, J. Yang, and S. Jana, “Deepxplore: Automated whitebox testing of deep learning systems,” in proceedings of the 26th Symposium on Operating Systems Principles , 2017, pp. 1–18
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2018
Cited alongside, same era.
X. Hu, G. Li, X. Xia, D. Lo, and Z. Jin, “Deep code comment generation,” in 2018 IEEE/ACM 26th International Conference on Program Comprehension (ICPC) . IEEE, 2018, pp. 200–20 010
2018
Cited alongside, same era.
Y. Liang and K. Q. Zhu, “Automatic generation of text descriptive comments for code blocks,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Cited alongside, same era.
L. Ma, F. Juefei-Xu, F. Zhang, J. Sun, M. Xue, B. Li, C. Chen, T. Su, L. Li, Y. Liu et al. , “Deepgauge: Multi-granularity testing criteria for deep learning systems,” in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering , 2018, pp. 120–131
2018
Cited alongside, same era.
2019
Later among the works it cites.
M. Allamanis, “The adverse effects of code duplication in machine learning models of code,” in Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software , 2019, pp. 143–153
2019
Later among the works it cites.
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-attention generative adversarial networks,” in International Conference on Machine Learning , 2019, pp. 7354–7363
2019
Later among the works it cites.
A. Odena, C. Olsson, D. Andersen, and I. Goodfellow, “Tensorfuzz: Debugging neural networks with coverage-guided fuzzing,” in International Conference on Machine Learning , 2019, pp. 4901–4911
2019
Later among the works it cites.
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Y. Tian, K. Pei, S. Jana, and B. Ray, “Deeptest: Automated testing of deep-neural-network-driven autonomous cars,” in Proceedings of the 40th international conference on software engineering , 2018, pp. 303–314
2018
Cited alongside, same era.
2018
Cited alongside, same era.
U. Alon, M. Zilberstein, O. Levy, and E. Yahav, “A general path-based representation for predicting program properties,” ACM SIGPLAN Notices , vol. 53, no. 4, pp. 404–419, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
J. Guo, Y. Jiang, Y. Zhao, Q. Chen, and J. Sun, “Dlfuzz: Differential fuzzing testing of deep learning systems,” in Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2018, pp. 739–743
2018
Cited alongside, same era.
C. E. Tuncali, G. Fainekos, H. Ito, and J. Kapinski, “Simulation-based adversarial test generation for autonomous vehicles with machine learning components,” in 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2018, pp. 1555–1562
2018
Cited alongside, same era.
U. Alon, M. Zilberstein, O. Levy, and E. Yahav, “code2vec: Learning distributed representations of code,” Proceedings of the ACM on Programming Languages , vol. 3, no. POPL, pp. 1–29, 2019
2019
Cited alongside, same era.
M. Chen and X. Wan, “Neural comment generation for source code with auxiliary code classification task,” in 2019 26th Asia-Pacific Software Engineering Conference (APSEC) . IEEE, 2019, pp. 522–529
2019
Cited alongside, same era.
J. Wang, G. Dong, J. Sun, X. Wang, and P. Zhang, “Adversarial sample detection for deep neural network through model mutation testing,” in 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 2019, pp. 1245–1256
2019
Later among the works it cites.
M. Nejadgholi and J. Yang, “A study of oracle approximations in testing deep learning libraries,” in 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2019, pp. 785–796
2019
Later among the works it cites.
D. Gopinath, C. S. Pasareanu, K. Wang, M. Zhang, and S. Khurshid, “Symbolic execution for attribution and attack synthesis in neural networks,” in 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) . IEEE, 2019, pp. 282–283
2019
Later among the works it cites.
M. A. Alcorn, Q. Li, Z. Gong, C. Wang, L. Mai, W.-S. Ku, and A. Nguyen, “Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4845–4854
2019
Later among the works it cites.
Q. Hu, L. Ma, X. Xie, B. Yu, Y. Liu, and J. Zhao, “Deepmutation++: A mutation testing framework for deep learning systems,” in 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2019, pp. 1158–1161
2019
Later among the works it cites.
2020
Later among the works it cites.
X. Hu, G. Li, X. Xia, D. Lo, and Z. Jin, “Deep code comment generation with hybrid lexical and syntactical information,” Empirical Software Engineering , vol. 25, no. 3, pp. 2179–2217, 2020
2020
Later among the works it cites.
H. Zhang, Z. Li, G. Li, L. Ma, Y. Liu, and Z. Jin, “Generating adversarial examples for holding robustness of source code processing models,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 01, 2020, pp. 1169–1176
2020
Later among the works it cites.
M. Vahdat Pour, Z. Li, L. Ma, and H. Hemmati, “A search-based testing framework for deep neural networks of source code embedding,” arXiv e-prints , pp. arXiv–2101, 2021
2021
Closest in time.
M. Allamanis, H. Peng, and C. Sutton, “A convolutional attention network for extreme summarization of source code,” in International conference on machine learning , 2016, pp. 2091–2100
2091
Closest in time.