LLM-Based Auto-Remediation Model for DevOps Pipeline Failures

Abstract

DevOps pipelines are the backbone of modern software delivery. They make it very easy to quickly, automatically, and continuously integrate and deploy across many other different cloud environments. However, these pipelines often fail because of configuration drifts, dependency mismatches, infrastructure inconsistencies as well as code-level errors. This causes operational bottlenecks that slow down releases along with their require a lot of human intervention. Standard remediation techniques rely heavily on these pre-written scripts, rigid rule-based frameworks, or engineers doing manual diagnostics. These methods often don't work well with the changing nature of toolchains and multi-cloud architectures. Recent progress in Large Language Models (LLMs) could make these pipelines more reliable by using context-aware reasoning, advanced root-cause analysis, and automated solution formulation. This work presents an LLM-based auto-remediation methodology designed to detect pipeline failures, analyze logs, correlate error patterns, provide corrective actions, and when feasible autonomously rectify issues in actual time. The proposed paradigm integrates natural language understanding with DevOps telemetry, source code analysis & configuration verification to connect detection as well as resolution. Experimental evaluations of widely used these CI/CD systems demonstrate substantial improvements in mean time to recovery (MTTR), a reduction in repetitive manual debugging tasks, and enhanced their pipeline dependability, especially in complex cloud-native workflows. The LLM-driven system is better than typical remediation scripts because it can adapt to the latest types of failures without needing to be reprogrammed, provide better information about the context of mistakes, and works well across teams & environments. This study shows how LLMs can change DevOps operations from being reactive to proactive, cut down on interruptions & let engineers focus on coming up with the latest ideas instead of putting out fires. This is a huge step toward self-healing automation in modern software delivery ecosystems.