Replication package for the paper: Applying Bandit Algorithms to Personalize Code Readability Evaluation by LLMs

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Autore principale: anonymous
Natura: Recurso digital
Pubblicazione: Zenodo 2025
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contents <p><span lang="EN-US">The scripts are worked on Windows 10 or later version. Some paths included in the scripts should be modified before the execution. Microsoft Excel and R Language are required to execute the scripts.</span></p> <p><span lang="EN-US"> </span></p> <p><span lang="EN-US">0. script.txt: template script for script.xlsx</span></p> <p><span lang="EN-US">1. master.xlsx: used to make learning and test dataset.</span></p> <p><span lang="EN-US">2. script.xlsx: used to make R scripts. By the script, calibration model of each developer is made.</span></p> <p><span lang="EN-US">3. dist.txt: used to calculate similarity (i.e., Euclidean distance) of each developer. The script works on R.</span></p> <p><span lang="EN-US">4. ba_m.txt: used to perform the bandit algorithm. The script works on R.</span></p>
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publishDate 2025
publisher Zenodo
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spellingShingle Replication package for the paper: Applying Bandit Algorithms to Personalize Code Readability Evaluation by LLMs
anonymous
<p><span lang="EN-US">The scripts are worked on Windows 10 or later version. Some paths included in the scripts should be modified before the execution. Microsoft Excel and R Language are required to execute the scripts.</span></p> <p><span lang="EN-US"> </span></p> <p><span lang="EN-US">0. script.txt: template script for script.xlsx</span></p> <p><span lang="EN-US">1. master.xlsx: used to make learning and test dataset.</span></p> <p><span lang="EN-US">2. script.xlsx: used to make R scripts. By the script, calibration model of each developer is made.</span></p> <p><span lang="EN-US">3. dist.txt: used to calculate similarity (i.e., Euclidean distance) of each developer. The script works on R.</span></p> <p><span lang="EN-US">4. ba_m.txt: used to perform the bandit algorithm. The script works on R.</span></p>
title Replication package for the paper: Applying Bandit Algorithms to Personalize Code Readability Evaluation by LLMs
url https://doi.org/10.5281/zenodo.15607299