loading page

On the Sustainability of Deep Learning Projects: Maintainers’ Perspective
  • +3
  • Junxiao Han,
  • Jiakun Liu,
  • David Lo,
  • Chen Zhi,
  • Yishan Chen,
  • Shuiguang Deng
Junxiao Han
Hangzhou City University
Author Profile
Jiakun Liu
Singapore Management University

Corresponding Author:[email protected]

Author Profile
David Lo
Singapore Management University
Author Profile
Chen Zhi
Zhejiang University
Author Profile
Yishan Chen
JiangXi University of Science and Technology
Author Profile
Shuiguang Deng
Zhejiang University
Author Profile


Deep learning (DL) techniques have grown in leaps and bounds in both academia and industry over the past few years. Despite the growth of DL projects, there has been little study on how DL projects evolve, whether maintainers in this domain encounter a dramatic increase in workload, and whether or not existing maintainers can guarantee the sustained development of projects. To address this gap, we perform an empirical study to investigate the sustainability of DL projects, understand maintainers’ workloads and workloads growth in DL projects, and compare them with traditional OSS projects. In this regard, we first investigate how DL projects grow, then, understand maintainers’ workload in DL projects, and explore the workload growth of maintainers as DL projects evolve. After that, we mine the relationships between maintainers’ activities and the sustainability of DL projects. Eventually, we compare it with traditional OSS projects. Our study unveils that although DL projects show increasing trends in most activities, maintainers’ workloads present a decreasing trend. Meanwhile, the proportion of workload maintainers conducted in DL projects is significantly lower than in traditional OSS projects. Moreover, there are positive and moderate correlations between the sustainability of DL projects and the number of maintainers’ releases, pushes, and merged pull requests. Our findings shed lights that help understand maintainers’ workload and growth trends in DL and traditional OSS projects, and also highlight actionable directions for organizations, maintainers, and researchers.
07 Aug 2023Submitted to Journal of Software: Evolution and Process
07 Aug 2023Submission Checks Completed
07 Aug 2023Assigned to Editor
09 Aug 2023Reviewer(s) Assigned
29 Sep 2023Review(s) Completed, Editorial Evaluation Pending
29 Sep 2023Editorial Decision: Revise Minor
17 Oct 20231st Revision Received
25 Oct 2023Submission Checks Completed
25 Oct 2023Assigned to Editor
28 Oct 2023Reviewer(s) Assigned
19 Nov 2023Review(s) Completed, Editorial Evaluation Pending
20 Nov 2023Editorial Decision: Accept