AI-Powered Anomaly Detection in CI/CD Pipelines Internship

About Internship

Associate Professor, Department of SoCS, from University of Petroleum and Energy Studies, Dehradun (UPES Dehradun), is accepting interns interested in the field of Computer Science Engineering, in the research area of Software Engineering.

Continuous Integration and Continuous Deployment (CI/CD) pipelines are prone to failures due to code inconsistencies, infrastructure issues, and misconfigurations. This research investigates the application of AI-driven anomaly detection techniques to predict and prevent build and deployment failures in DevOps pipelines. The proposed system leverages machine learning models trained on historical build logs, test reports, and deployment statuses to detect anomalies and forecast potential failures. The study implements an AI-powered anomaly detection module using Scikit-learn, TensorFlow, and ELK Stack integrated into Jenkins/GitHub Actions pipelines. Experimental evaluation demonstrates how AI-based predictive analytics can reduce pipeline failures and enhance software reliability. The research concludes with a discussion on the challenges of AI adoption in real-world DevOps environments and the future of self-healing CI/CD systems.