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:

analysis.ipynb - Notebook containing the data analysis presented in the paper, as well as some additional inquiries.

data - Data generated from the experiences described in the paper, the extracted Python code, and the results for running said code.

full_non_agg.xlsx - Non-aggregated results extracted from the generated data.

LICENSE_CODE.txt - The license for the included code.

LICENSE_DATA.txt - The license for the included data.

README.md - This file.

requirements.txt- Python dependencies required for running the included notebook.

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 Electronics, 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).