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All Journal The Eastasouth Journal of Information System and Computer Science
Kakarla, Roshan
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Computer Science & IT

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Ai-Driven Devops Automation for Ci/Cd Pipeline Optimization Kakarla, Roshan; Sannareddy, Sai Bharath
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 01 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i01.849

Abstract

Modern CI/CD pipelines have become cognitively overloaded, policy-fragile, and operationally inefficient as software delivery scales across microservices, multi-cloud platforms, and regulated environments. While DevOps automation has improved deployment velocity, it remains largely rule-based, reactive, and incapable of reasoning over complex pipeline behavior, failure patterns, or governance constraints. This paper introduces a systemic, AI-driven DevOps automation framework designed to optimize CI/CD pipelines through continuous learning, risk-aware decision-making, and policy-aligned control. The core contribution is a closed-loop, intelligence-driven control plane that integrates telemetry inference, pipeline behavior modeling, and constrained decision automation to optimize build reliability, deployment throughput, and operational toil while preserving human oversight and enterprise governance. Unlike existing approaches that focus on isolated optimizations or tool-level enhancements, the proposed framework treats CI/CD as a distributed socio-technical system, addressing failure modes related to scale, drift, cognitive load, and compliance. We describe the architecture, lifecycle control flow, and governance mechanisms of the proposed system, and evaluate its impact using operational metrics such as mean time to detection (MTTD), mean time to recovery (MTTR), pipeline failure recurrence, and policy deviation rates. The results demonstrate that AI-driven DevOps automation, when designed as a governed control system rather than an autonomous executor, can materially improve reliability, safety, and delivery efficiency in enterprise CI/CD environments.
LLM-Based Autonomous Remediation for DevSecOps Pipelines Kakarla, Roshan
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 02 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i02.856

Abstract

Modern DevSecOps pipelines operate at a scale and velocity that exceeds the cognitive and operational capacity of traditional rule-based automation and human-centric incident response. While monitoring, alerting, and security scanning tools have matured, remediation remains largely manual, fragmented, and reactive resulting in prolonged mean time to resolution (MTTR), configuration drift, and governance gaps. This paper proposes a novel LLM-Based Autonomous Remediation Framework (LLM-ARF) that introduces a risk-aware, policy-governed control plane for automated detection, diagnosis, and remediation across DevSecOps pipelines. Unlike existing approaches that rely on static runbooks or narrow AI classifiers, LLM-ARF integrates large language models as reasoning agents embedded within a constrained, auditable, and human-supervised execution loop. The framework explicitly separates cognition, decision authority, and actuation, enabling scalable autonomy while preserving accountability and compliance. We present the architectural design, lifecycle control flow, and governance mechanisms of LLM-ARF, and evaluate its operational impact using real-world DevOps metrics such as MTTR reduction, alert fatigue mitigation, and toil reduction. The results demonstrate that LLM-ARF enables a step-function improvement in remediation reliability without compromising safety or human oversight, positioning autonomous remediation as a viable next evolution of enterprise DevSecOps systems.
Co-Authors Sannareddy, Sai Bharath