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Many works have recently proposed the use of Large Language Model (LLM) based agents for performing `repository level' tasks, loosely defined as a set of tasks whose scopes are greater than a single file.
2005
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G. Amati, BM25 . Boston, MA: Springer US, 2009, pp. 257–260. [Online]. Available: https://doi.org/10.1007/978-0-387-39940-9_921
2009
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2017
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2017
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T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
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E. Aghajani, C. Nagy, M. Linares-Vásquez, L. Moreno, G. Bavota et al. , “Software documentation: the practitioners’ perspective,” in Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering , 2020, pp. 590–601
2020
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2021
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J. Y. Khan, M. T. I. Khondaker, G. Uddin, and A. Iqbal, “Automatic detection of five api documentation smells: Practitioners’ perspectives,” in 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2021, pp. 318–329
2021
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J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph et al. , “Emergent abilities of large language models,” Transactions on Machine Learning Research , 2022, survey Certification. [Online]. Available: https://openreview.net/forum?id=yzkSU5zdwD
2022
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C. S. Xia and L. Zhang, “Less training, more repairing please: revisiting automated program repair via zero-shot learning,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2022, pp. 959–971
2022
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J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022
2022
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S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran et al. , “React: Synergizing reasoning and acting in language models,” in Proceedings of the International Conference on Learning Representation , ser. ICLR 2022, 2022
2022
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A. Fan, B. Gokkaya, M. Harman, M. Lyubarskiy, S. Sengupta et al. , “Large language models for software engineering: Survey and open problems,” in Proceedings of the 45th IEEE/ACM International Conference on Software Engineering: Future of Software Engineering , ser. ICSE-FoSE, May 2023, pp. 31–53
2023
Cited alongside, same era.
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 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) , 2023, pp. 919–931
2023
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S. Kang, G. An, and S. Yoo, “A quantitative and qualitative evaluation of llm-based explainable fault localization,” Proceedings of the ACM on Software Engineering , vol. 1, no. FSE, pp. 1424–1446, 2024
2024
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J. Yoon, R. Feldt, and S. Yoo, “Intent-driven mobile gui testing with autonomous large language model agents,” in Proceedings of the 16th IEEE International Conference on Software Testing, Verification and Validation , ser. ICST 2024, 2024
2024
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2024
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R. Bairi, A. Sonwane, A. Kanade, A. Iyer, S. Parthasarathy et al. , “Codeplan: Repository-level coding using llms and planning,” Proceedings of the ACM on Software Engineering , vol. 1, no. FSE, pp. 675–698, 2024
2024
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2023
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2023
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2023
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2023
Cited alongside, same era.
2023
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
C. S. Xia, M. Paltenghi, J. Le Tian, M. Pradel, and L. Zhang, “Fuzz4all: Universal fuzzing with large language models,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , ser. ICSE ’24. New York, NY, USA: Association for Computing Machinery, 2024. [Online]. Available: https://doi.org/10.1145/3597503.3639121
2024
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2024
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2024
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N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua et al. , “Lost in the middle: How language models use long contexts,” Transactions of the Association for Computational Linguistics , vol. 12, pp. 157–173, 2024
2024
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2024
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2024
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2024
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C. Utrilla Guerrero, O. Corcho, and D. Garijo, “Automated extraction of research software installation instructions from readme files: An initial analysis,” in International Workshop on Natural Scientific Language Processing and Research Knowledge Graphs , 2024, pp. 114–133
2024
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