Enhancing Genetic Improvement Mutations Using Large Language Models

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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)

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2021

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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

2021

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

2023

Cited alongside, same era.

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)

2023

Cited alongside, same era.

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

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2023

Closest in time.

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

2023

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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)

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2023

Closest in time.

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)

2023

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