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.