naist-se / newcomer-oss-candidate Goto Github PK

The ability of an Open Source Software (OSS) project to attract, onboard, and retain any newcomer is vital to its livelihood. Although, evidence suggests an upsurge in novice developers joining social coding platforms (such as GitHub), the extent to which their activities result in a OSS contribution is unknown. Henceforth, we execute the protocols of a registered report to study activities of a "Newcomer OSS-Candidate", who is a novice developer that is new to that social coding platform, and has the intention to later onboard an OSS project. Using GitHub as a case platform, we analyze 171 identified Newcomer OSS-Candidates to characterize their contribution activities. Results show that Newcomer OSS-Candidates are likely to target software based repositories (i.e., 66%), and their first contributions are mainly associated with development (commits) and maintenance (PRs). Newcomer OSS-Candidates are less likely to practice social coding, but eventually end up onboarding (i.e., 30% quantitative, 70% follow-up survey) an OSS project. Furthermore, they cite finding a way to start as the most challenging barrier to contribute. Our work reveals insights on how newcomers to social coding platforms are potential sources of OSS contributions.

📁 Newcomer Candidate project/
├─ 📁 Data/
├─ 📁 Scripts/ 
├─ 📁 Survey/
─

1. Data- is a folder that contains the dataset for Newcomer Candidate project.
2. Scripts- is a folder that contains the all the codes.
3. Survey- is a folder that contains the survey obtained from participants.

1. Clone the repository from here and the Dataset from here

2. Extract the files.

3. Open Jupyter Notebook or Python Spyder.

4. Copy any code and Set your working directory using

          ```
          import os
          
          #Please specify your dataset directory. 
          os.chdir("..../Dataset/")
          ```
   

1. IFraz Rehman, Nara Institute of Science and Technology (NAIST), Nara, Japan.
2. Dong Wang, Nara Institute of Science and Technology (NAIST), Nara, Japan.
3. Raula Gaikovina Kula, Nara Institute of Science and Technology (NAIST), Nara, Japan.
4. Takashi Ishio, Nara Institute of Science and Technology (NAIST), Nara, Japan.
5. Kenichi Matsumoto, Nara Institute of Science and Technology (NAIST), Nara, Japan.

Our SZZ implementation is licensed under the MIT License.

CC0 summary and legal text

Our dataset are published on the public domain, so that anyone may freely build upon, enhance and reuse the dataset for any purposes without restriction under copyright or database law.