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(2022). https:\/\/openai.com\/blog\/chatgpt\/."},{"key":"e_1_3_2_1_66_1","unstructured":"Max Sch\u00e4fer Sarah Nadi Aryaz Eghbali and Frank Tip. 2023. Adaptive Test Generation Using a Large Language Model. arXiv:2302.06527 [cs.SE]"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3517034"},{"key":"e_1_3_2_1_68_1","volume-title":"Eric Wallace, and Sameer Singh.","author":"Shin Taylor","year":"2020","unstructured":"Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020. Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020)."},{"key":"e_1_3_2_1_69_1","volume-title":"Fuzzing: Brute Force Vulnerability Discovery","author":"Sutton Michael","year":"2007","unstructured":"Michael Sutton, Adam Greene, and Pedram Amini. 2007. Fuzzing: Brute Force Vulnerability Discovery. 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