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Enhancing Genetic Improvement Mutations: Acknowledgements & Referencesby@mutation
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Enhancing Genetic Improvement Mutations: Acknowledgements & References

by Mutation Technology PublicationsFebruary 27th, 2024
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Explore the intersection of large language models and software engineering, delving into enhancements in genetic improvement mutations, automated bug fixing performance evaluation using ChatGPT, and the advancements and challenges of universal fuzzing. TLDR: The paper examines the impact of large language models on software engineering, covering topics such as enhancing genetic improvement mutations, evaluating ChatGPT's automatic bug fixing performance, and exploring the advancements and challenges of universal fuzzing.
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Authors:

(1) Alexander E.I. Brownlee, University of Stirling, UK;

(2) James Callan, University College London, UK;

(3) Karine Even-Mendoza, King’s College London, UK;

(4) Alina Geiger, Johannes Gutenberg University Mainz, Germany;

(5) Justyna Petke, University College London, UK;

(6) Federica Sarro, University College London, UK;

(7) Carol Hanna, University College London, UK;

(8) Dominik Sobania, Johannes Gutenberg University Mainz, Germany.

Abstract & Introduction

Experimental Setup

Results

Conclusions and Future Work

Acknowledgements & References

Acknowledgments

UKRI EPSRC EP/P023991/1 and ERC 741278.

References

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This paper is available on arxiv under CC 4.0 license.