Understanding newcomers' onboarding process in deep learning projects

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 s...

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Main Authors: HAN, Junxiao, ZHANG, Jiahao, LO, David, XIA, Xin, DENG, Shuigang, WU, Minghui
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/8663
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spelling sg-smu-ink.sis_research-96662024-02-22T03:00:04Z Understanding newcomers' onboarding process in deep learning projects HAN, Junxiao ZHANG, Jiahao LO, David XIA, Xin DENG, Shuigang WU, Minghui 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. 2024-01-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/8663 info:doi/10.1109/TSE.2024.3353297 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Deep learning Deep Learning Projects Documentation Libraries Market research Newcomer Onboarding Open source software Open Source Software Software development management Tutorials Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Deep learning
Deep Learning Projects
Documentation
Libraries
Market research
Newcomer Onboarding
Open source software
Open Source Software
Software development management
Tutorials
Software Engineering
spellingShingle Deep learning
Deep Learning Projects
Documentation
Libraries
Market research
Newcomer Onboarding
Open source software
Open Source Software
Software development management
Tutorials
Software Engineering
HAN, Junxiao
ZHANG, Jiahao
LO, David
XIA, Xin
DENG, Shuigang
WU, Minghui
Understanding newcomers' onboarding process in deep learning projects
description 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.
format text
author HAN, Junxiao
ZHANG, Jiahao
LO, David
XIA, Xin
DENG, Shuigang
WU, Minghui
author_facet HAN, Junxiao
ZHANG, Jiahao
LO, David
XIA, Xin
DENG, Shuigang
WU, Minghui
author_sort HAN, Junxiao
title Understanding newcomers' onboarding process in deep learning projects
title_short Understanding newcomers' onboarding process in deep learning projects
title_full Understanding newcomers' onboarding process in deep learning projects
title_fullStr Understanding newcomers' onboarding process in deep learning projects
title_full_unstemmed Understanding newcomers' onboarding process in deep learning projects
title_sort understanding newcomers' onboarding process in deep learning projects
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/8663
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