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Although the open source model bears many advantages in software development,open source projects are always hard to sustain. Previous research on opensource sustainability mainly focuses on projects that have already reached acertain level of maturity (e.g., with communities, releases, and downstreamprojects). However, limited attention is paid to the development of(sustainable) open source projects in their infancy, and we believe anunderstanding of early sustainability determinants is crucial for projectinitiators, incubators, newcomers, and users.  In this paper, we aim to explore the relationship between early participationfactors and long-term project sustainability. We leverage a novel methodologycombining the Blumberg model of performance and machine learning to predict thesustainability of 290,255 GitHub projects. Specificially, we train an XGBoostmodel based on early participation (first three months of activity) in 290,255GitHub projects and we interpret the model using LIME. We quantitatively showthat early participants have a positive effect on project's future sustainedactivity if they have prior experience in OSS project incubation anddemonstrate concentrated focus and steady commitment. Participation fromnon-code contributors and detailed contribution documentation also promoteproject's sustained activity. Compared with individual projects, building acommunity that consists of more experienced core developers and more activeperipheral developers is important for organizational projects. This studyprovides unique insights into the incubation and recognition of sustainableopen source projects, and our interpretable prediction approach can also offerguidance to open source project initiators and newcomers.

How Early Participation Determines Long-Term Sustained Activity in GitHub Projects?