Supplementary material for "DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct code for LoRaWAN-related engineering tasks"

Overview This package contains the following files and folders: data - Data generated from the experiences described in the paper, the extracted Python code, and the results for running said code. LICENSE_CODE.txt - The license for the included code. LICENSE_DATA.txt - The license for the included data. README.md - This file. requirements.txt- Specifies the Python dependencies required for running the included notebook. analysis.ipynb - Notebook containing the data analysis presented in the paper, as well as some additional inquiries. Reproducibility of data analysis The data analysis presented in the research paper "DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct code for LoRaWAN-related engineering tasks", published in TBD, and authored by Daniel Fernandes, João P. Matos-Carvalho, Carlos M. Fernandes, and Nuno Fachada, can be reproduced with the Jupyter notebook included in this package. Licenses The code in the Jupyter notebook is made available under the MIT license (see LICENSE_CODE.txt). The non-code materials are made available under a CC-BY 4.0 license (see LICENSE_DATA.txt).