- author = "Sungmin Kang and Shin Yoo",
- title = "Towards Objective-Tailored Genetic Improvement Through Large Language Models",
- booktitle = "12th International Workshop on Genetic Improvement @ICSE 2023",
- year = "2023",
- editor = "Vesna Nowack and Markus Wagner and Gabin An and Aymeric Blot and Justyna Petke",
- pages = "19--20",
- address = "Melbourne, Australia",
- month = "20 " # may,
- publisher = "IEEE",
- note = "{Best position paper}",
- keywords = "genetic algorithms, genetic programming, Genetic Improvement, optimisation, AI, ANN, LLM, LLM+GI, code-davinci-002 OpenAI, Python Fibonacci, execution time, memory consumption",
- isbn13 = "979-8-3503-1232-4",
-
URL = "
https://arxiv.org/abs/2304.09386",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gi2023/Kang_2023_GI.pdf",
-
DOI = "
doi:10.1109/GI59320.2023.00013",
-
slides_url = "http://gpbib.cs.ucl.ac.uk/gi2023/Kang_GI2023.pdf",
-
video_url = "http://gpbib.cs.ucl.ac.uk/gi2023/Kang_GI2023.mp4",
-
video_url = "https://www.youtube.com/watch?v=k3AkQ9liAJc&list=PLI8fiFpB7BoJLh6cUpGBjyeB1hM9DET1V&index=6",
- size = "2 pages",
- abstract = "While Genetic Improvement (GI) is a useful paradigm to improve functional and nonfunctional aspects of software, existing techniques tended to use the same set of mutation operators for differing objectives, due to the difficulty of writing custom mutation operators. we suggest that Large Language Models (LLMs) can be used to generate objective-tailored mutants, expanding the possibilities of software optimisations that GI can perform. We further argue that LLMs and the GI process can benefit from the strengths of one another, and present a simple example demonstrating that LLMs can both improve the effectiveness of the GI optimization process, while also benefiting from the evaluation steps of GI. As a result, we believe that the combination of LLMs and GI has the capability to significantly aid developers in optimizing their software.",
-
notes = "Sungmin Kang also won the best presentation
award.
'one can ask an LLM to change code in a specific manner using natural language'
GI @ ICSE 2023, part of \cite{Nowack:2023:GI}",
