International Journal of Science, Engineering and TechnologyAuthors: Harish Reddy, Meenakshi Das, Kavitha Murugan, Suresh Balan
Abstract: The increasing complexity and velocity of patch management in enterprise Red Hat environments necessitates a shift from traditional static testing to intelligent, predictive methodologies. Patch deployment especially involving kernel updates, shared libraries, or core packages can introduce performance regressions, configuration conflicts, or application downtime, particularly in mission-critical systems. This review explores the application of machine learning (ML) techniques to assess and predict the impact of patches before deployment. By analyzing system logs, resource metrics, historical incident reports, and patch metadata, ML models can provide proactive insights into risk levels associated with specific updates. The article outlines a multi-phase architecture for implementing ML-driven patch analysis, including data collection from Red Hat systems (e.g., journalctl, auditd, YUM logs), feature engineering, supervised and unsupervised modeling, and integration into continuous delivery pipelines. Special emphasis is placed on explainability, time-series forecasting, and the importance of retraining to accommodate evolving patch behaviors. The review also discusses challenges such as data sparsity, inconsistent logging formats, and model generalization across Red Hat workloads in production, development, and containerized environments. Future directions include reinforcement learning for patch sequencing, cross-platform federated learning, and AI-driven test orchestration. By embedding machine learning into patch management workflows, organizations can achieve more resilient, compliant, and efficient Red Hat operations while minimizing service disruptions and administrative burden
DOI: http://doi.org/10.5281/zenodo.15846870
