Machine Learning for Software Deployment in the Public Sector: A Systematic Review and Research Agenda for African Contexts
DOI:
https://doi.org/10.63158/journalisi.v8i4.1839Keywords:
Machine Learning Deployment Support, Software Deployment Decision Support, Systematic Literature Review, Public Sector Digital Services, African Digital TransformationAbstract
Failures in deploying digital public services can disrupt essential systems and affect millions of citizens. Machine learning (ML)-based software deployment decision support, including build risk prediction, release gating, autoscaling, rollback assistance, and post-deployment anomaly detection, offers opportunities for safer and more reliable releases. However, the extent of existing evidence in public-sector environments, particularly within African institutions, remains unclear. Following the PRISMA 2020 guidelines, this systematic literature review searched five databases using predefined inclusion criteria and a six-item quality assessment. A total of 33 peer-reviewed studies published between 2018 and 2025 were included, while studies focusing exclusively on MLOps were excluded. The findings reveal that none of the reviewed studies (0/33) explicitly evaluated ML deployment decision support in public-sector contexts or African institutions; existing evidence originates primarily from private-sector or unspecified environments. Research efforts are concentrated on autoscaling (12/33, 36%) and build prediction (9/33, 27%), with tree-based models being the dominant approach (16/33, 48%). Furthermore, only one study (3%) reported statistical significance testing or confidence intervals. This review identifies a research and evidence gap rather than confirming the absence of practical adoption. It proposes a staged research agenda toward explainable, lightweight, and context-aware ML deployment support for African public institutions.
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