JONNALA, R., YANG, J., LEE, Y., LIANG, G. und CAO, Z., 2025. Measuring and Improving the Efficiency of Python Code Generated by LLMs Using Co T Prompting and Fine-Tuning. IEEE Access, Access, IEEE. 1 Januar 2025. Vol. 13, , p. 119657-119681. DOI 10.1109/ACCESS.2025.3585742.
Elsevier - Harvard (with titles)Jonnala, R., Yang, J., Lee, Y., Liang, G., Cao, Z., 2025. Measuring and Improving the Efficiency of Python Code Generated by LLMs Using Co T Prompting and Fine-Tuning. IEEE Access, Access, IEEE 13, 119657-119681. https://doi.org/10.1109/ACCESS.2025.3585742
American Psychological Association 7th editionJonnala, R., Yang, J., Lee, Y., Liang, G., & Cao, Z. (2025). Measuring and Improving the Efficiency of Python Code Generated by LLMs Using Co T Prompting and Fine-Tuning. IEEE Access, Access, IEEE, 13, 119657-119681. https://doi.org/10.1109/ACCESS.2025.3585742
Springer - Basic (author-date)Jonnala R, Yang J, Lee Y, Liang G, Cao Z (2025) Measuring and Improving the Efficiency of Python Code Generated by LLMs Using Co T Prompting and Fine-Tuning. IEEE Access, Access, IEEE 13:119657-119681. https://doi.org/10.1109/ACCESS.2025.3585742
Juristische Zitierweise (Stüber) (Deutsch)Jonnala, R./ Yang, J./ Lee, Y./ Liang, G./ Cao, Z., Measuring and Improving the Efficiency of Python Code Generated by LLMs Using Co T Prompting and Fine-Tuning, IEEE Access, Access, IEEE 2025, 119657-119681.