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Majdinasab, F. Khomh, and M. C. Desmarais. &quot;Effective test generation using pre-trained Large Language Models and mutation testing&quot;. Inf. Softw. Technol. (2024), 171. doi: https://doi.org/10.1016/j.infsof.2024.107468</unstructured_citation></citation><citation key="ref3"><doi>10.1145/3639478.3639789</doi><unstructured_citation>A. Deljouyi. &quot;Understandable Test Generation Through Capture/Replay and LLMs&quot;. In Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion '24). Association for Computing Machinery, New York, NY, USA, 2024, pp261-263. doi: https://doi.org/10.1145/3639478.3639789</unstructured_citation></citation><citation key="ref4"><doi>10.1109/TSE.2024.3368208</doi><unstructured_citation>W. Junjie, Y. Huang, C. Chen, Z. Liu, S. Wang and Q. 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Chen. &quot;LLM for Test Script Generation and Migration: Challenges, Capabilities, and Opportunities&quot;. 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS) (2023), pp 206-217. doi: https://doi.org/10.1109/QRS60937.2023.00029</unstructured_citation></citation><citation key="ref11"><doi>10.1109/TSE.2024.3382365</doi><unstructured_citation>Y. Tang, Z. Liu, Z. Zhou and X. Luo, &quot;ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation&quot;. in IEEE Transactions on Software Engineering, vol. 50, no. 06, (2024), pp. 1340-1359. doi: https://doi.org/10.1109/TSE.2024.3382365</unstructured_citation></citation><citation key="ref12"><doi>10.35940/ijrte.C8142.13030924</doi><unstructured_citation>Pesati, N. (2024). Security Considerations for Large Language Model Use: Implementation Research in Securing LLM-Integrated Applications. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 13, Issue 3, pp. 19-27). https://doi.org/10.35940/ijrte.c8142.13030924</unstructured_citation></citation><citation key="ref13"><doi>10.35940/ijsce.D3636.14030724</doi><unstructured_citation>Lalaei, R. A., &amp; Mahmoudabadi, Dr. A. (2024). Promoting Project Outcomes: A Development Approach to Generative AI and LLM-Based Software Applications' Deployment. In International Journal of Soft Computing and Engineering (Vol. 14, Issue 3, pp. 6-13). https://doi.org/10.35940/ijsce.d3636.14030724</unstructured_citation></citation></citation_list>
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