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Pre-trained giant code models (PCMs) start coming into the developers' daily practices.
H. Diel, “Language representation based on abstract sytax,” in GI — 6. Jahrestagung , E. J. Neuhold, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 1976, pp. 133–147
1976
Earlier work this paper cites.
J. Conrad, The secret sharer . phonereader, 2006
2006
Earlier work this paper cites.
S. Thummalapenta and T. Xie, “Parseweb: a programmer assistant for reusing open source code on the web,” in Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering , 2007, pp. 204–213
2007
Earlier work this paper cites.
Z. Xing and E. Stroulia, “Api-evolution support with diff-catchup,” IEEE Transactions on Software Engineering , vol. 33, no. 12, pp. 818–836, 2007
2007
Earlier work this paper cites.
P. Godefroid, P. de Halleux, A. V. Nori, S. K. Rajamani, W. Schulte, N. Tillmann, and M. Y. Levin, “Automating software testing using program analysis,” IEEE software , vol. 25, no. 5, pp. 30–37, 2008
2008
Earlier work this paper cites.
B. Dagenais and L. J. Hendren, “Enabling static analysis for partial java programs,” Proceedings of the 23rd ACM SIGPLAN conference on Object-oriented programming systems languages and applications , 2008
2008
Earlier work this paper cites.
P. T. Devanbu, “On the naturalness of software,” 2012 34th International Conference on Software Engineering (ICSE) , pp. 837–847, 2012
2012
Earlier work this paper cites.
R. Robbes, M. Lungu, and D. Röthlisberger, “How do developers react to api deprecation?: the case of a smalltalk ecosystem,” in SIGSOFT FSE , 2012
2012
Earlier work this paper cites.
S. Subramanian, L. Inozemtseva, and R. Holmes, “Live api documentation,” International Conference on Software Engineering (ICSE) , 2014
2014
Earlier work this paper cites.
R. Pawlak, M. Monperrus, N. Petitprez, C. Noguera, and L. Seinturier, “Spoon: A Library for Implementing Analyses and Transformations of Java Source Code,” Software: Practice and Experience , vol. 46, pp. 1155–1179, 2015. [Online]. Available: https://hal.archives-ouvertes.fr/hal-01078532/document
2015
Earlier work this paper cites.
X. Ye, H. Shen, X. Ma, R. C. Bunescu, and C. Liu, “From word embeddings to document similarities for improved information retrieval in software engineering,” 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE) , pp. 404–415, 2016
2016
Earlier work this paper cites.
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep contextualized word representations,” in NAACL , 2018
2018
Earlier work this paper cites.
A. Radford and K. Narasimhan, “Improving language understanding by generative pre-training,” in . , 2018
2018
Earlier work this paper cites.
M. Allamanis, E. T. Barr, P. T. Devanbu, and C. Sutton, “A survey of machine learning for big code and naturalness,” ACM Computing Surveys (CSUR) , vol. 51, pp. 1 – 37, 2018
2018
Earlier work this paper cites.
T. Zhang, G. Upadhyaya, A. Reinhardt, H. Rajan, and M. Kim, “Are code examples on an online q&a forum reliable?: A study of api misuse on stack overflow,” 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) , pp. 886–896, 2018
2018
Earlier work this paper cites.
M. M. Rahman, J. Barson, S. Paul, J. Kayani, F. A. Lois, S. F. Quezada, C. Parnin, K. T. Stolee, and B. Ray, “Evaluating how developers use general-purpose web-search for code retrieval,” in Proceedings of the 15th International Conference on Mining Software Repositories , 2018, pp. 465–475
2018
Earlier work this paper cites.
D. Gopstein, H. H. Zhou, P. Frankl, and J. Cappos, “Prevalence of confusing code in software projects: Atoms of confusion in the wild,” in Proceedings of the 15th International Conference on Mining Software Repositories , 2018, pp. 281–291
2018
Earlier work this paper cites.
P. Yin, B. Deng, E. Chen, B. Vasilescu, and G. Neubig, “Learning to mine aligned code and natural language pairs from stack overflow,” in 2018 IEEE/ACM 15th international conference on mining software repositories (MSR) . IEEE, 2018, pp. 476–486
2018
Earlier work this paper cites.
Z. Su, G. Zhang, F. Yue, L. Chang, J. Jiang, and X. Yao, “Snr-constrained heuristics for optimizing the scaling parameter of robust audio watermarking,” IEEE Transactions on Multimedia , vol. 20, pp. 2631–2644, 2018
2018
Earlier work this paper cites.
H. D. Phan, H. A. Nguyen, N. M. Tran, L.-H. Truong, A. T. Nguyen, and T. N. Nguyen, “Statistical learning of api fully qualified names in code snippets of online forums,” 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) , pp. 632–642, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
W. Li, C. J. Mitchell, and T. Chen, “Your code is my code: Exploiting a common weakness in oauth 2.0 implementations,” in Cambridge International Workshop on Security Protocols . Springer, 2018, pp. 24–41
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
Z. Yang, Z. Dai, Y. Yang, J. G. Carbonell, R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” in NeurIPS , 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
C. M. K. Saifullah, M. Asaduzzaman, and C. K. Roy, “Learning from examples to find fully qualified names of api elements in code snippets,” in 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) , 2019, pp. 243–254
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. S. Rathore and S. Kumar, “A study on software fault prediction techniques,” Artificial Intelligence Review , vol. 51, no. 2, pp. 255–327, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2021
Later among the works it cites.
M. Lamothe, Y.-G. Guéhéneuc, and W. Shang, “A systematic review of api evolution literature,” ACM Computing Surveys (CSUR) , vol. 54, no. 8, pp. 1–36, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
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J. Zhang, X. Wang, H. Zhang, H. Sun, K. Wang, and X. Liu, “A novel neural source code representation based on abstract syntax tree,” in 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 2019, pp. 783–794
2019
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
X. Ren, X. Ye, Z. Xing, X. Xia, X. Xu, L. Zhu, and J. Sun, “Api-misuse detection driven by fine-grained api-constraint knowledge graph,” 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) , pp. 461–472, 2020
2020
Cited alongside, same era.
L. T. C. Melo, R. G. Ribeiro, B. C. F. Guimarães, and F. M. Q. a. Pereira, “Type inference for c: Applications to the static analysis of incomplete programs,” ACM Trans. Program. Lang. Syst. , 2020
2020
Cited alongside, same era.
L. Li, J. Gao, T. F. Bissyandé, L. Ma, X. Xia, and J. Klein, “Cda: Characterising deprecated android apis,” Empirical Software Engineering , vol. 25, pp. 2058–2098, 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
Z. Zhao, E. Wallace, S. Feng, D. Klein, and S. Singh, “Calibrate before use: Improving few-shot performance of language models,” in International Conference on Machine Learning . PMLR, 2021, pp. 12 697–12 706
2021
Later among the works it cites.
J. D. Weisz, M. J. Muller, S. Houde, J. T. Richards, S. I. Ross, F. Martinez, M. Agarwal, and K. Talamadupula, “Perfection not required? human-ai partnerships in code translation,” 26th International Conference on Intelligent User Interfaces , 2021
2021
Later among the works it cites.
Z. Li, D. Zou, S. Xu, H. Jin, Y. Zhu, and Z. Chen, “Sysevr: A framework for using deep learning to detect software vulnerabilities,” IEEE Transactions on Dependable and Secure Computing , 2021
2021
Later among the works it cites.
N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, U. Erlingsson et al. , “Extracting training data from large language models,” in 30th USENIX Security Symposium (USENIX Security 21) , 2021, pp. 2633–2650
2021
Later among the works it cites.
H. Pearce, B. Ahmad, B. Tan, B. Dolan-Gavitt, and R. Karri, “An empirical cybersecurity evaluation of github copilot’s code contributions,” arXiv e-prints , pp. arXiv–2108, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
Y. Wan, W. Zhao, H. Zhang, Y. Sui, G. Xu, and H. Jin, “What do they capture? - a structural analysis of pre-trained language models for source code,” 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) , pp. 2377–2388, 2022
2022
Closest in time.
X. Yuan, G. Lin, Y. Tai, and J. Zhang, “Deep neural embedding for software vulnerability discovery: Comparison and optimization,” Security and Communication Networks , vol. 2022, 2022
2022
Closest in time.
D. Wang, Z. Jia, S. Li, Y. Yu, Y. Xiong, W. Dong, and X. Liao, “Bridging pre-trained models and downstream tasks for source code understanding,” 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) , pp. 287–298, 2022
2022
Closest in time.
2022
Closest in time.
Q. Huang, Z. Yuan, Z. Xing, X. Xu, L. Zhu, and Q. Lu, “Prompt-tuned code language model as a neural knowledge base for type inference in statically-typed partial code,” International Conference on Automated Software Engineering(ASE) , 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
Y. Dong, T. Gu, Y. Tian, and C. Sun, “Snr: Constraint-based type inference for incomplete java code snippets,” 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) , pp. 1982–1993, 2022
2022
Closest in time.
2022
Closest in time.
Y. Dong, T. Gu, Y. Tian, and C. Sun, “Snr: Constraint based type inference for incomplete java code snippets,” International Conference on Software Engineering (ICSE) , 2022
2022
Closest in time.
H. Pearce, B. Ahmad, B. Tan, B. Dolan-Gavitt, and R. Karri, “Asleep at the keyboard? assessing the security of github copilot’s code contributions,” in 2022 IEEE Symposium on Security and Privacy (SP) . IEEE, 2022, pp. 754–768
2022
Closest in time.
P. Vaithilingam, T. Zhang, and E. L. Glassman, “Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models,” in CHI Conference on Human Factors in Computing Systems Extended Abstracts , 2022, pp. 1–7
2022
Closest in time.
M. Lee, P. Liang, and Q. Yang, “Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities,” in CHI Conference on Human Factors in Computing Systems , 2022, pp. 1–19
2022
Closest in time.
T. Wu, M. Terry, and C. J. Cai, “Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts,” in CHI Conference on Human Factors in Computing Systems , 2022, pp. 1–22
2022
Closest in time.