Publication Type
Journal Article
Version
submittedVersion
Publication Date
11-2023
Abstract
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 open-source software (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.
Keywords
deep learning, maintainers, sustainability, workload
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Journal of Software: Evolution and Process
First Page
1
Last Page
20
ISSN
2047-7481
Identifier
10.1002/smr.2645
Publisher
Wiley
Citation
HAN, Junxiao; LIU, Jiakun; LO, David; ZHI, Chen; CHEN, Yishan; and DENG, Shuiguang.
On the sustainability of deep learning projects: Maintainers' perspective. (2023). Journal of Software: Evolution and Process. 1-20.
Available at: https://ink.library.smu.edu.sg/sis_research/8481
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1002/smr.2645