nkiru-ede/MavenNetworkStudy

This repository contains Maven repository data extracted in 2018 by Benelallam et al. The repo contains the Maven artifacts (Source artifacts), their dependencies (Target artifacts), and their release dates.

To illustrate the growth of Maven repository, across the years, we have included three datasets - GAV1(GAV1.tsv), GAV2(GAV2.tsv) and GAV3(GAV3.tsv) - the three datasets shows the maven artifacts, their dependencies and release dates.

To get a true sense of the actual growth of the Maven repository, we look further into the aggregation of GAV that ignores version (GA) and artifact id (G) - two data sets show this - GA.tsv contains maven artifacts (without versions), their dependencies and release dates

G.tsv contains the maven groups (without versions and artifact id), their dependencies and release dates

Schema_G.tsv, Schema_GA.tsv, Schema_GAV.tsv - outlines the nature of all datasets

Steps to replicate:

Reproducing Results

Prerequisites

Use Python version 3.*, tested with 3.12.2

Install git (any version)

Run the below commands to install git lfs and pull datasets links_all and release_all

git lfs install
git lfs pull

Note: To install dependencies on MacOs, you may need to use 'brew' command

Steps

Step 1: Acquire dataset from Benellallam et al paper paper and build GAV directed graph

Input	Output
TODO: Zenodo dataset	`Project/data/GAV`
Script	Project/datawrang.py

Step 2: Aggregated GAV graph to GA and G graphs as described in paper section TODO

Script	Input	Output
`Project/aggregate_ga.py`, `Project/aggregate_g.py`	`Project/data/GAV`	`Project/data/GA.csv`, `Project/data/G.csv`, `Project/plot`
Tests	test/test_data	`test\test_aggregate_ga.py`, `test_aggregate_g.py`

Step 3: Compute Transitive Closures for GAV/GA/G

Script	Input	Output
RemoveLoops_Cycles.py	TODO: Zenodo dataset	`Project/data/cleaned_data`
SCC.py	Project/data/cleaned_data	Project/data/condensed_dag
Transitive_deps.py	Project/data/condensed_dag	Project/data/transitive_dependencies
`datawrang_trans.py`	`Project/data/transitive_dependencies`	`Project/data/GAV`
`Project/aggregate_ga.py`, `Project/aggregate_g.py`	Project/data/GAV	`Project/data/GA.csv`, `Project/data/G.csv`, `Project/plot`

Running the Project on a Windows Machine

Ensure Python is installed:
- If not installed, download and install Python from python.org.
Open Command Prompt:
- Press Win + R, type cmd, and press Enter.
- Navigate to the directory where you want to clone the repository and execute command:

git clone https://github.com/nkiru-ede/MavenNetworkStudy.git

Navigate to the repository directory:
```
cd MavenNetworkStudy\Project
```
Install dependencies:

pip install -r requirements.txt

or 

pip3 install -r requirements.txt

Run the python scripts

python datawrang.py
python aggregate_ga.py
python aggregate_g.py

Run tests

cd test
python test_aggregate_ga.py
python test_aggregate_g.py

Running the Project on a MacOS

MacOS usually comes with a Python pre-installed, you can check installed version by executing below command:

python --version

- Navigate to the directory where you want to clone the repository and execute command:

git clone https://github.com/nkiru-ede/MavenNetworkStudy.git

Navigate to the repository directory:
```
cd MavenNetworkStudy\Project
```
Install dependencies:

pip install -r requirements.txt

or 

pip3 install -r requirements.txt

Run the python scripts

python datawrang.py
python aggregate_ga.py
python aggregate_g.py