# Predicting Open Source Software Sustainability with Deep Temporal Neural Hierarchical Architectures and Explainable AI

arXiv: https://arxiv.org/abs/2602.09064 · HTML: https://mlsanity.com/paper/2602.09064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 51 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 15 of 15)

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- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
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- [Making Online Communities 'Better': A Taxonomy of Community Values on Reddit](https://mlsanity.com/paper/2109.05152/) (2021)
- [Emoji Promotes Developer Participation and Issue Resolution on GitHub](https://mlsanity.com/paper/2308.16360/) (2023)
- [A Temporal Neural Network Architecture for Online Learning](https://mlsanity.com/paper/2011.13844/) (2020)
- [Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning](https://mlsanity.com/paper/2410.13792/) (2024)
- [Classifying Developers into Core and Peripheral: An Empirical Study on Count and Network Metrics](https://mlsanity.com/paper/1604.00830/) (2016)
- [Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic](https://mlsanity.com/paper/2301.12326/) (2023)
- [Sustainability Forecasting for Apache Incubator Projects](https://mlsanity.com/paper/2105.14252/) (2021)
- [An Open-Source and Reproducible Implementation of LSTM and GRU Networks for Time Series Forecasting](https://mlsanity.com/paper/2504.18185/) (2025)

## Abstract (arXiv)

Open Source Software (OSS) projects follow diverse lifecycle trajectories shaped by evolving patterns of contribution, coordination, and community engagement. Understanding these trajectories is essential for stakeholders seeking to assess project organization and health at scale. However, prior work has largely relied on static or aggregated metrics, such as project age or cumulative activity, providing limited insight into how OSS sustainability unfolds over time. In this paper, we propose a hierarchical predictive framework that models OSS projects as belonging to distinct lifecycle stages grounded in established socio-technical categorizations of OSS development. Rather than treating sustainability solely as project longevity, these lifecycle stages operationalize sustainability as a multidimensional construct integrating contribution activity, community participation, and maintenance dynamics. The framework combines engineered tabular indicators with 24-month temporal activity sequences and employs a multi-stage classification pipeline to distinguish lifecycle stages associated with different coordination and participation regimes. To support transparency, we incorporate explainable AI techniques to examine the relative contribution of feature categories to model predictions. Evaluated on a large corpus of OSS repositories, the proposed approach achieves over 94% overall accuracy in lifecycle stage classification. Attribution analyses consistently identify contribution activity and community-related features as dominant signals, highlighting the central role of collective participation dynamics.