AI-Powered Automation in Software Development and Deployment through AutoDevOps

The integration of Artificial Intelligence (AI) into software development and deployment processes is revolutionizing the way applications are built, tested, and delivered. This research paper explores the concept of AI-powered automation within the AutoDevOps framework, which merges DevOps practices with intelligent systems to streamline the entire Software Development Life Cycle (SDLC). AutoDevOps leverages machine learning, predictive analytics, and intelligent orchestration to automate critical tasks such as code integration, testing, monitoring, and deployment, reducing manual effort and accelerating release cycles. This study investigates how AI enhances AutoDevOps by enabling adaptive decision-making, real-time anomaly detection, and resource optimization in cloud-native environments. Tools that utilize AI for continuous integration/continuous deployment (CI/CD), security analysis, and performance tuning are examined to understand their impact on productivity, code quality, and operational efficiency. Real-world case studies and survey data from industry professionals are analyzed to assess the effectiveness and adoption of AI-driven AutoDevOps solutions. In addition to highlighting the benefits, the paper also addresses key challenges, including model reliability, data privacy, integration complexity, and the evolving role of human oversight. The findings suggest that AI-augmented AutoDevOps represents a significant leap forward in modern software engineering practices, offering organizations a scalable, intelligent, and resilient approach to software delivery. This research contributes to a deeper understanding of how AI is shaping the future of automated development and deployment ecosystems.