Data for DEXPRO

DEXPRO (Diversity-aware EXpertise-based Project Recommender of Open source) Tool Source Code, Build scripts, and Demo

DEXPRO is a novel tool for addressing the "cold-start" problem of recommending OSS projects to newcomers with no previous interaction with OSS projects or existing social connections with other developers. It is an expertise-based project recommender for OSS Newcomers created by adapting the Skill Space model.

The scripts for building the DEXPRO framework from raw World of Code data are shared in the "WoC_scripts.zip" file - see the "README.md" file in the folder for running instructions. 

The source code for the DEXPRO tool is contained in the "DEXPRO_tool.zip" file. 

The demo is in the "DEXPRO_DEMO.mov" file.

How to run the tool:

1. Make sure you have Python 3.9+ installed.
2. Create a virtual environment, activate, and install the required packages:

• $ python3 -m venv venv

• $ source venv/bin/activate

• $ pip3 install -r requirements.txt

1. Run the streamlit app:       

• $ streamlit run app.py

All the tool options can be seen in the tool demo video.

DEXPRO filters & parameters:

Trained Skill Space model from World of Code version U, trained on:

• Projects with >=10 stars, >= 1 year active, last activity on or after 2021-06-01 
• Authors with >100 commits & <50,000 commits, 
• 12 popular Languages: 'C/C++', 'C#', 'Go', 'Perl', 'Ruby', 'JavaScript','Python', 'R', 'Rust', 'Scala', 'TypeScript', 'Java'

Parameters:

• vector size = 200
• window size = 30
• negative sample = 20
• min count = 1000
• epoch = 1

* Evaluation data

The evaluation data is added in the eval.zip folder.

It contains scripts for how the list of authors for the evaluation were obtained from World of Code, the data on the languages & APIs used by the authors' first commits and script for how that data was obtained. The list of commits and projects for the authors were obtained directly from World of Code a2c and a2p maps and that data is also shared. 

The Mann-Whitney test result can be seen from the `eval.ipynb` file.

The feedback from the 20 students is in the `DEXPRO STUDENT FEEDBACK.docx` file.