Enhancing Genetic Improvement Mutations: Acknowledgements & References

Written by mutation | Published 2024/02/27
Tech Story Tags: large-language-models | genetic-improvement | genetic-improvement-mutations | llms-for-genetic-improvement | gpt3.5-for-genetic-improvement | llm-applications | llm-research-papers | generic-programming

TLDRExplore 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.via the TL;DR App

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.

Table of Links

Abstract & Introduction

Experimental Setup

Results

Conclusions and Future Work

Acknowledgements & References

Acknowledgments

UKRI EPSRC EP/P023991/1 and ERC 741278.

References

  1. Artifact of Enhancing Genetic Improvement Mutations Using Large Language Models. Zenodo (Sep 2023). https://doi.org/10.5281/zenodo.8304433

  2. B¨ohme, M., Soremekun, E.O., Chattopadhyay, S., Ugherughe, E., Zeller, A.: Where is the bug and how is it fixed? An experiment with practitioners. In: Proc. ACM Symposium on the Foundations of Software Engineering. pp. 117–128 (2017)

  3. Brownlee, A.E., Petke, J., Alexander, B., Barr, E.T., Wagner, M., White, D.R.: Gin: genetic improvement research made easy. In: GECCO. pp. 985–993 (2019)

  4. Brownlee, A.E., Petke, J., Rasburn, A.F.: Injecting shortcuts for faster running Java code. In: IEEE CEC 2020. p. 1–8

  5. Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021)

  6. Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., Zhang, J.M.: Large language models for software engineering: Survey and open problems (2023)

  7. Github - jcodec/jcodec: Jcodec main repo, https://github.com/jcodec/jcodec

  8. Han, S.J., Ransom, K.J., Perfors, A., Kemp, C.: Inductive reasoning in humans and large language models. Cognitive Systems Research p. 101155 (2023)

  9. Hou, X., Liu, Y., Yang, Z., Grundy, J., Zhao, Y., Li, L., Wang, K., Luo, X., Lo, D., Wang, H.: Large language models for software engineering: A systematic literature review. arXiv:2308.10620 (2023)

  10. Kang, S., Yoo, S.: Towards objective-tailored genetic improvement through large language models. arXiv:2304.09386 (2023)

  11. Kim, D., Nam, J., Song, J., Kim, S.: Automatic Patch Generation Learned from Human-Written Patches (2013), http://logging.apache.org/log4j/

  12. Kirbas, S., Windels, E., Mcbello, O., Kells, K., Pagano, M., Szalanski, R., Nowack, V., Winter, E., Counsell, S., Bowes, D., Hall, T., Haraldsson, S., Woodward, J.: On the introduction of automatic program repair in bloomberg. IEEE Software 38(4), 43–51 (2021)

  13. Marginean, A., Bader, J., Chandra, S., Harman, M., Jia, Y., Mao, K., Mols, A., Scott, A.: Sapfix: Automated end-to-end repair at scale. In: ICSE-SEIP. pp. 269– 278 (2019)

  14. Petke, J., Alexander, B., Barr, E.T., Brownlee, A.E., Wagner, M., White, D.R.: Program transformation landscapes for automated program modification using Gin. Empirical Software Engineering 28(4), 1–41 (2023)

  15. Petke, J., Haraldsson, S.O., Harman, M., Langdon, W.B., White, D.R., Woodward, J.R.: Genetic improvement of software: A comprehensive survey. IEEE Transactions on Evolutionary Computation 22, 415–432 (2018)

  16. Siddiq, M.L., Santos, J., Tanvir, R.H., Ulfat, N., Rifat, F.A., Lopes, V.C.: Exploring the effectiveness of large language models in generating unit tests. arXiv preprint arXiv:2305.00418 (2023)

  17. Sobania, D., Briesch, M., Hanna, C., Petke, J.: An analysis of the automatic bug fixing performance of chatgpt. In: 2023 IEEE/ACM International Workshop on Automated Program Repair (APR). pp. 23–30. IEEE Computer Society (2023)

  18. Xia, C.S., Paltenghi, M., Tian, J.L., Pradel, M., Zhang, L.: Universal fuzzing via large language models. arXiv preprint arXiv:2308.04748 (2023)

  19. Xia, C.S., Zhang, L.: Keep the conversation going: Fixing 162 out of 337 bugs for $0.42 each using chatgpt. arXiv preprint arXiv:2304.00385 (2023)

This paper is available on arxiv under CC 4.0 license.


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Published by HackerNoon on 2024/02/27