This is Team Balloon's project for the MSR Virtual Hackathon 2022.
We analysed reciprocity in the code review process. Using vector autoregressive (VAR) models over GitHub's pull request data, we explored if there is a causal relationship between: 1) reviews performed by a mantainer and 2) the acceptance of their own code contributions.
This repository contains the Python scripts for performing such analysis.
The code was tested on MacOS using Anaconda Python 3.9 and ElasticSearch 7.15.2. You need to
install ElasticSearch in your
system before running the scripts.
Before running the scripts, please start ElasticSearch and update the global variable ELASTICSEARCH_HOST on the
config.py file with your ElasticSearch URL.
To install Python dependencies, execute the following:
pip install -r requirements.txt
Finally, we use GitHub's API ,via Perceval, to obtain
pull-request data.
For this to work, we need you to obtain a personal access token
from your GitHub account and set it to the global variable GITHUB_API_TOKEN on the config.py file.
The file dataloading.py contains functions for retriving pull-request from GitHub and
storing it in ElasticSearch. The following snippet retrieves
pull-requests for the Kubernetes repository, created over the last 10 years:
from dataloading import get_and_store
def star_extraction(owner: str, repository: str):
get_and_store(owner, repository, factor=0, new_index=True)
for year_factor in range(1, 10):
get_and_store(owner, repository, factor=year_factor, new_index=False)
if __name__ == "__main__":
star_extraction(owner="kubernetes", repository="kubernetes")This code will store the data in a ElasticSearch index called kubernetes-kubernetes.
To perform the reciprocity analysis over this project, we can use the functions from
the devanalysis.py file:
from typing import Tuple
import elasticsearch
from elasticsearch import Elasticsearch
from aggregation import PRS_REVIEWED_AND_MERGED, PRS_AUTHORED_AND_MERGED
from config import ELASTICSEARCH_HOST
from devanalysis import analyse_project
if __name__ == "__main__":
elastic_search: Elasticsearch = elasticsearch.Elasticsearch(ELASTICSEARCH_HOST)
variables: Tuple = (PRS_REVIEWED_AND_MERGED, PRS_AUTHORED_AND_MERGED)
project_prs, project_analysis = analyse_project(elastic_search, "kubernetes-kubernetes", "month", variables,
"aic")This code will generate monthly time series from the data stored in the kubernetes-kubernetes index.
Regarding the order of the VAR model, it will choose the value with the minimum Akaike Information Criteria (AIC).
Per developer, plots will be stored at the img folder and the output of statistical tests at txt.
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.