Reliability Models in Automated Release Cycles

Abstract

This article presents an analysis of existing reliability assurance models within automated release cycles (CI/CD), covering a spectrum from classical rule-based and monitoring-oriented approaches to modern AI-accelerated AIOps solutions. Based on a comprehensive literature review, the study outlines a conceptual architecture for an AI orchestration layer that integrates data collection, predictive analytics, automated self-healing, and continuous retraining of ML modules. It is demonstrated that implementing the proposed model reduces mean time to detection (MTTD), decreases mean time to recovery (MTTR), and increases release frequency compared to traditional practices. The paper also discusses key aspects of ML model version management, Explainable AI, and potential directions for future research. The insights regarding reliability models in automated release cycles will be of interest to DevOps engineers and software reliability specialists applying stochastic methods and formal verification techniques to minimize risks during continuous deployment. The material will also be valuable for researchers and graduate students in the field of distributed microservice architecture resilience, particularly those working on integrating Bayesian predictive models with the formalization of service level agreements (SLA) within DevSecOps processes.