This repository contains the machine learning part on the use of machine learning methods to recommend software refactoring.
- The paper can be found here: https://arxiv.org/abs/2001.03338
- The raw dataset can be found here: https://zenodo.org/record/3547639
- The appendix with our full results can be found here: https://zenodo.org/record/3583980
This project contains all the Python scripts that are responsible for the ML pipeline.
This project requires python 3.6 or higher.
First, install all the dependencies:
pip3 install --user -r requirements.txt
Then, create a dbconfig.ini file, following the example structure in
dbconfig-example.ini. In this file, you configure your database connection.
Finally, configure the training in the configs.py. There, you can define which datasets to analyze, which models to build, which under sampling algorithms to use, and etc. Please, read the comments in this file.
The main Python script that generates all the models and results is the
binary_classification.py. You run it by simply calling python3 binary_classification.py.
The results will be stored in a results/ folder.
The generated output is a text file with a weak structure. A quick way to get results is by grepping:
cat *.txt | grep "CSV": returns a CSV with all the models and their precision, recall, and accuracy.cat *.txt | grep "TIME": returns a CSV with how much time it took to train and test the model.
Before running the pipeline, we suggest you to warm up the cache. The warming up basically executes all the required queries and cache them as CSV files. These queries can take a long time to run... and if you are like us, you will most likely re-execute your experiments many times! :) Thus, having them cached helps:
python3 warm_cache.py
If you need to clean up the cache, simply delete the _cache directory.
This project was initially envisioned by Maurício Aniche, Erick Galante Maziero, Rafael Durelli, and Vinicius Durelli.
This project is licensed under the MIT license.