An Exploratory Study on what defines a feature from issue label as feature and their characteristics

This is a step by step guide to recreate the dataset and experiments that we performed for this paper.

Setting up the Anaconda Environment

1. conda env create --file environment.yml

2. conda activate plot

3. jupyter notebook

Seed data analysis

We got our preliminary dataset (result.csv) which already had data on repositories with labels named feature from SEART using the GUI https://seart-ghs.si.usi.ch/

Steps to reproduce our seed data

1. Execute the code in the file seed_data_analysis.ipynb to fetch all the repos with label named feature and having more than 500 stars with totalIssues greater than 20. The result of the seed data analysis is saved in seed_dataset.csv

Methodology 1

The research was broken into two methodology to achieve result, see the folder methodology 1

Methodology 2

The research was broken into two methodology to achieve result, see the folder methodology 2

Visualisation

The visualisation of the pipelines are the .png files visible across different folders in the project.