AI IN DEVOPS: A FRAMEWORK FOR PREDICTIVE MAINTENANCE AND AUTOMATED ISSUE RESOLUTION

Abstract

The rapid evolution of DevOps approaches has changed the software development lifecycle by enabling faster delivery, continuous integration, and continuous deployment. Notwithstanding these advances, traditional DevOps techniques still suffer from reactive incident management, prolonged downtime, and inadequate foresight into system failures. Often referred to as AIOps, the integration of artificial intelligence (AI) into DevOps provides a powerful solution by enabling predictive maintenance and automated issue resolution. By means of an in-depth review of peer-reviewed literature, this work investigates the terrain of AI-driven technologies used in DevOps, including anomaly detection, log analysis, root cause localization, and trace-based learning. Inspired by insights gained from past studies and observed gaps, we propose a novel AI-augmented DevOps framework that continuously adapts via feedback loops and proactively forecasts faults and automates corrective action. Using this framework, which offers a strategic road map for intelligent automation in modern DevOps pipelines, mean time to resolution (MTTR) should be reduced, system resilience should be enhanced, and operational efficiency raised.