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Large Language Models (LLMs) have shown promising results in automatic code generation by improving coding efficiency to a certain extent.
J. L. Fleiss, “Measuring nominal scale agreement among many raters.” Psychological bulletin , vol. 76, no. 5, p. 378, 1971
1971
Earlier work this paper cites.
F. Wilcoxon, Individual comparisons by ranking methods . Springer, 1992
1992
Earlier work this paper cites.
E. W. Weisstein, “Bonferroni correction,” https://mathworld. wolfram. com/ , 2004
2004
Earlier work this paper cites.
G. Fraser and A. Arcuri, “Evosuite: automatic test suite generation for object-oriented software,” in Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering , 2011, pp. 416–419
2011
Earlier work this paper cites.
M. Harman, P. McMinn, J. T. De Souza, and S. Yoo, “Search based software engineering: Techniques, taxonomy, tutorial,” Empirical Software Engineering and Verification: International Summer Schools, LASER 2008-2010, Elba Island, Italy, Revised Tutorial Lectures , pp. 1–59, 2012
2012
Earlier work this paper cites.
A. Arcuri and G. Fraser, “Parameter tuning or default values? an empirical investigation in search-based software engineering,” Empirical Software Engineering , vol. 18, no. 3, pp. 594–623, 2013
2013
Earlier work this paper cites.
R. Singh and N. S. Mangat, Elements of survey sampling . Springer Science & Business Media, 2013, vol. 15
2013
Earlier work this paper cites.
D. Sena, R. Coelho, U. Kulesza, and R. Bonifácio, “Understanding the exception handling strategies of java libraries: An empirical study,” in Proceedings of the 13th International Conference on Mining Software Repositories , 2016, pp. 212–222
2016
Earlier work this paper cites.
A. Panichella, F. M. Kifetew, and P. Tonella, “Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets,” IEEE Transactions on Software Engineering , vol. 44, no. 2, pp. 122–158, 2017
2017
Earlier work this paper cites.
E. A. Barbosa and A. Garcia, “Global-aware recommendations for repairing violations in exception handling,” in Proceedings of the 40th International Conference on Software Engineering , 2018, pp. 858–858
2018
Earlier work this paper cites.
G. B. de Pádua and W. Shang, “Studying the relationship between exception handling practices and post-release defects,” in Proceedings of the 15th International Conference on Mining Software Repositories , 2018, pp. 564–575
2018
Earlier work this paper cites.
H. Li, S. Li, J. Sun, Z. Xing, X. Peng, M. Liu, and X. Zhao, “Improving api caveats accessibility by mining api caveats knowledge graph,” in 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 2018, pp. 183–193
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
T. Nguyen, P. Vu, and T. Nguyen, “Recommending exception handling code,” in 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 2019, pp. 390–393
2019
Earlier work this paper cites.
J. Zhang, X. Wang, H. Zhang, H. Sun, Y. Pu, and X. Liu, “Learning to handle exceptions,” in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering , 2020, pp. 29–41
2020
Earlier work this paper cites.
X. Ren, J. Sun, Z. Xing, X. Xia, and J. Sun, “Demystify official api usage directives with crowdsourced api misuse scenarios, erroneous code examples and patches,” in Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering , 2020, pp. 925–936
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
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,” in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering , 2020, pp. 461–472
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
H. Huang, M. Wen, L. Wei, Y. Liu, and S.-C. Cheung, “Characterizing and detecting configuration compatibility issues in android apps,” in 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2021, pp. 517–528
2021
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
E. Nijkamp, B. Pang, H. Hayashi, L. Tu, H. Wang, Y. Zhou, S. Savarese, and C. Xiong, “A conversational paradigm for program synthesis,” arXiv e-prints , pp. arXiv–2203, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
2023
Closest in time.
S. Barke, M. B. James, and N. Polikarpova, “Grounded copilot: How programmers interact with code-generating models,” Proceedings of the ACM on Programming Languages , vol. 7, no. OOPSLA1, pp. 85–111, 2023
2023
Closest in time.
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T. Wu, M. Terry, and C. J. Cai, “Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts,” in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems , 2022, pp. 1–22
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Y. Li, D. Choi, J. Chung, N. Kushman, J. Schrittwieser, R. Leblond, T. Eccles, J. Keeling, F. Gimeno, A. Dal Lago et al. , “Competition-level code generation with alphacode,” Science , vol. 378, no. 6624, pp. 1092–1097, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
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
Cited alongside, same era.
X. Liu, K. Ji, Y. Fu, W. Tam, Z. Du, Z. Yang, and J. Tang, “P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , 2022, pp. 61–68
2022
Cited alongside, same era.
2023
Closest in time.
2023
Closest in time.
J. Leinonen, A. Hellas, S. Sarsa, B. Reeves, P. Denny, J. Prather, and B. A. Becker, “Using large language models to enhance programming error messages,” in Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 , 2023, pp. 563–569
2023
Closest in time.
N. Nashid, M. Sintaha, and A. Mesbah, “Retrieval-based prompt selection for code-related few-shot learning,” in Proceedings of the 45th International Conference on Software Engineering (ICSE’23) , 2023
2023
Closest in time.
JavaDoc, “Java official sdk & jdk api documentation,” https://docs.oracle.com/en/java/javase/17/docs/api/index.html , 2023
2023
Closest in time.
KPC, “Kpc replication package,” https://github.com/goodchar123/KPC , 2023
2023
Closest in time.
Tutorialspoint, “Tutorialspoint,” https://www.tutorialspoint.com/javaexamples/index.htm , 2023
2023
Closest in time.
JavaAPI, “The exception handling specification for java.util.vector.get(int index),” https://docs.oracle.com/en/java/javase/17/docs/api/java.base/java/util/Vector.html#get(int) , 2023
2023
Closest in time.
——, “The exception handling specification for java.util.vector.set(int index, e element),” https://docs.oracle.com/en/java/javase/17/docs/api/java.base/java/util/Vector.html#set(int,E) , 2023
2023
Closest in time.
——, “The exception handling specification for java.lang.string.split(string regex),” https://docs.oracle.com/en/java/javase/17/docs/api/java.base/java/lang/String.html#split(java.lang.String) , 2023
2023
Closest in time.
beautiful-soup 4, “Beautiful soup 4,” https://beautiful-soup-4.readthedocs.io/en/latest/ , 2023
2023
Closest in time.
Openai, “Gpt-3.5-turbo,” https://platform.openai.com/docs/models/gpt-3-5 , 2023
2023
Closest in time.
2023
Closest in time.
C. Lemieux, J. P. Inala, S. K. Lahiri, and S. Sen, “Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models,” in 45th International Conference on Software Engineering, ser. ICSE , 2023
2023
Closest in time.
Openai, “Github copilot,” https://github.com/features/copilot , 2023
2023
Closest in time.
Openai, “Introducing chatgpt,” https://openai.com/blog/chatgpt , 2023
2023
Closest in time.