AI-DRIVEN LOG ANALYTICS FOR CONTINUOUS INTEGRATION AND DEPLOYMENT MONITORING

Abstract

Continuous Integration and Continuous Deployment (CI/CD) pipelines have become fundamental components of modern DevOps practices, enabling automated software development, testing, and deployment processes. Despite their advantages in accelerating software delivery, CI/CD pipelines generate large volumes of operational logs that are often difficult to analyze manually. These logs contain critical information related to build processes, test execution, system failures, and deployment performance. Traditional log monitoring approaches rely on rule-based alerting systems that often fail to detect complex failure patterns or anomalies in real time. Consequently, integrating artificial intelligence (AI) and machine learning techniques into log analytics has emerged as a promising solution for improving CI/CD monitoring and operational reliability.

The present study explores the application of AI-driven log analytics for monitoring CI/CD pipelines and identifying anomalies within software delivery workflows. A simulated DevOps environment was developed to generate CI/CD pipeline logs consisting of build events, error logs, system metrics, and deployment records. Machine learning models including Random Forest, Support Vector Machine, and Gradient Boosting algorithms were applied to analyze log patterns and predict pipeline failures. Performance metrics such as anomaly detection accuracy, build failure prediction rate, and log classification precision were evaluated.

The results demonstrate that AI-based log analytics significantly improves CI/CD monitoring by enabling early detection of abnormal pipeline behavior and reducing troubleshooting time. The Gradient Boosting model achieved the highest anomaly detection accuracy of 94.1%, outperforming traditional rule-based monitoring systems. These findings highlight the potential of AI-driven log analytics to enhance operational visibility, improve system reliability, and support proactive DevOps monitoring strategies in complex software development environments.