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With the rise of large language models (LLMs), researchers are increasingly exploring their applications in var ious vertical domains, such as software engineering.
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2024
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2024
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2024
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2023
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S. Suri, S. N. Das, K. Singi, K. Dey, V. S. Sharma, and V. Kaulgud, “Software engineering using autonomous agents: Are we there yet?,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)
2023
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2023
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2023
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Y. Zhang, W. Song, Z. Ji, N. Meng, et al
2023
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S. Kang, J. Yoon, and S. Yoo, “Large language models are few-shot testers: Exploring llm-based general bug reproduction,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
2023
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2023
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2023
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2024
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M. H. Nguyen, T. P. Chau, P. X. Nguyen, and N. D. Q. Bui, “Agilecoder: Dynamic collaborative agents for software development based on agile methodology,” 2024
2024
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S. Manish, “An autonomous multi-agent llm framework for agile software development,” International Journal of Trend in Scientific Research and Development
2024
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2024
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T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, E. Hambro, L. Zettlemoyer, N. Cancedda, and T. Scialom, “Toolformer: Language models can teach themselves to use tools,” Advances in Neural Information Processing Systems
2024
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X. Wang, Y. Chen, L. Yuan, Y. Zhang, Y. Li, H. Peng, and H. Ji, “Executable code actions elicit better llm agents,” in Proceedings of the 41st International Conference on Machine Learning
2024
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F. Mu, L. Shi, S. Wang, Z. Yu, B. Zhang, C. Wang, S. Liu, and Q. Wang, “Clarifygpt: A framework for enhancing llm-based code generation via requirements clarification,” Proceedings of the ACM on Software Engineering
2024
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Just Accepted
X. Jiang, Y. Dong, L. Wang, F. Zheng, Q. Shang, G. Li, Z. Jin, and W. Jiao, “Self-planning code generation with large language models,” ACM Trans. Softw. Eng. Methodol · 2024
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2024
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2024
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F. Vallecillos Ruiz, “Agent-driven automatic software improvement,” in Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering
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D. Roy, X. Zhang, R. Bhave, C. Bansal, P. Las-Casas, R. Fonseca, and S. Rajmohan, “Exploring llm-based agents for root cause analysis,” in Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering
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S. Feng and C. Chen, “Prompting is all you need: Automated android bug replay with large language models,” in Proceedings of the 46th IEEE/ACM International Conference on Software Engineering
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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
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Y. Tang, Z. Liu, Z. Zhou, and X. Luo, “Chatgpt vs sbst: A comparative assessment of unit test suite generation,” IEEE Transactions on Software Engineering
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Q. Zhang, T. Zhang, J. Zhai, C. Fang, B. Yu, W. Sun, and Z. Chen, “A critical review of large language model on software engineering: An example from chatgpt and automated program repair,” 2024
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G. Lu, X. Ju, X. Chen, W. Pei, and Z. Cai, “Grace: Empowering llm-based software vulnerability detection with graph structure and in-context learning,” Journal of Systems and Software
2024
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2024
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R. Tóth, T. Bisztray, and L. Erdődi, “Llms in web development: Evaluating llm-generated php code unveiling vulnerabilities and limitations,” in International Conference on Computer Safety, Reliability, and Security
2024
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2024
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J. Zhang, J. P. Cambronero, S. Gulwani, V. Le, R. Piskac, G. Soares, and G. Verbruggen, “Pydex: Repairing bugs in introductory python assignments using llms,” Proceedings of the ACM on Programming Languages
2024
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E. Hilario, S. Azam, J. Sundaram, K. Imran Mohammed, and B. Shanmugam, “Generative ai for pentesting: the good, the bad, the ugly,” International Journal of Information Security
2024
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Y. Zhang, H. Ruan, Z. Fan, and A. Roychoudhury, “Autocoderover: Autonomous program improvement,” in Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis
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W. Tao, Y. Zhou, Y. Wang, W. Zhang, H. Zhang, and Y. Cheng, “Magis: Llm-based multi-agent framework for github issue resolution,” 2024
2024
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L. Zhang, K. Li, K. Sun, D. Wu, Y. Liu, H. Tian, and Y. Liu, “Acfix: Guiding llms with mined common rbac practices for context-aware repair of access control vulnerabilities in smart contracts,” 2024
2024
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F. Geissler, K. Roscher, and M. Trapp, “Concept-guided llm agents for human-ai safety codesign,” in Proceedings of the AAAI Symposium Series
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L. Zhong, Z. Wang, and J. Shang, “Debug like a human: A large language model debugger via verifying runtime execution step-by-step,” 2024
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N. Alshahwan, M. Harman, I. Harper, A. Marginean, S. Sengupta, and E. Wang, “Assured llm-based software engineering,” 2024
2024
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2025
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