Understanding newcomers' onboarding process in deep learning projects

• Main Authors: HAN, Junxiao, ZHANG, Jiahao, LO, David, XIA, Xin, DENG, Shuigang, WU, Minghui
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Deep learning; Deep Learning Projects; Documentation; Libraries; Market research; Newcomer Onboarding; Open source software; Open Source Software; Software development management; Tutorials; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8663
• Institution: Singapore Management University

Attracting and retaining newcomers are critical for the sustainable development of Open Source Software (OSS) projects. Considerable efforts have been made to help newcomers identify and overcome barriers in the onboarding process. However, fewer studies focus on newcomers’ activities before their successful onboarding. Given the rising popularity of deep learning (DL) techniques, we wonder what the onboarding process of DL newcomers is, and if there exist commonalities or differences in the onboarding process for DL and non-DL newcomers. Therefore, we reported a study to understand the growth trends of DL and non-DL newcomers, mine DL and non-DL newcomers’ activities before their successful onboarding (i.e., past activities), and explore the relationships between newcomers’ past activities and their first commit patterns and retention rates. By analyzing 20 DL projects with 9,191 contributors and 20 non-DL projects with 9,839 contributors, and conducting email surveys with contributors, we derived the following findings: 1) DL projects have attracted and retained more newcomers than non-DL projects. 2) Compared to non-DL newcomers, DL newcomers encounter more deployment, documentation, and version issues before their successful onboarding. 3) DL newcomers statistically require more time to successfully onboard compared to non-DL newcomers, and DL newcomers with more past activities (e.g., issues, issue comments, and watch) are prone to submit an intensive first commit (i.e., a commit with many source code and documentation files being modified). Based on the findings, we shed light on the onboarding process for DL and non-DL newcomers, highlight future research directions, and provide practical suggestions to newcomers, researchers, and projects.