Sustainability Forecasting for Apache Incubator Projects

TL;DR

This paper develops a socio-technical network model to predict Apache Incubator projects' sustainability with over 93% accuracy within 8 months, aiding early intervention and project success.

Contribution

It introduces an interpretable socio-technical modeling approach to forecast OSS project sustainability during incubation, enabling proactive management.

Findings

Models achieve over 93% accuracy in predicting sustainability.

Socio-technical network dynamics are key indicators of project success.

Strategy for real-time monitoring and intervention is proposed.

Abstract

Although OSS development is very popular, ultimately more than 80 percent of OSS projects fail. Identifying the factors associated with OSS success can help in devising interventions when a project takes a downturn. OSS success has been studied from a variety of angles, more recently in empirical studies of large numbers of diverse projects, using proxies for sustainability, e.g., internal metrics related to productivity and external ones, related to community popularity. The internal socio-technical structure of projects has also been shown important, especially their dynamics. This points to another angle on evaluating software success, from the perspective of self-sustaining and self-governing communities. To uncover the dynamics of how a project at a nascent development stage gradually evolves into a sustainable one, here we apply a socio-technical network modeling perspective to a dataset of Apache Software Foundation Incubator (ASFI), sustainability-labeled projects. To identify and validate the determinants of sustainability, we undertake a mix of quantitative and qualitative studies of ASFI projects' socio-technical network trajectories. We develop interpretable models which can forecast a project becoming sustainable with more than 93 percent accuracy, within 8 months of incubation start. Based on the interpretable models we describe a strategy for real-time monitoring and suggesting actions, which can be used by projects to correct their sustainability trajectories.