Predictive DevSecOps Intelligence with Machine Learning Based Failure Forecasting for Autonomous CI/CD Pipeline Optimization

Abstract

Modern software delivery environments rely heavily on Continuous Integration and Continuous Delivery (CI/CD) pipelines to automate source-code validation, security assessment, testing, packaging, deployment, and operational monitoring. However, increasing pipeline complexity, distributed development teams, heterogeneous infrastructure, frequent code changes, and security controls create numerous opportunities for build failures, test instability, deployment errors, resource bottlenecks, and pipeline interruptions. Predictive DevSecOps intelligence provides an opportunity to address these challenges by forecasting pipeline failures before they occur and autonomously optimizing execution strategies. This research proposes a machine learning based predictive DevSecOps framework that integrates historical pipeline telemetry, source-code characteristics, build metrics, test outcomes, dependency information, infrastructure conditions, and security findings to identify failure patterns and estimate failure probabilities. The proposed methodology combines data preprocessing, feature engineering, supervised machine learning, temporal analysis, anomaly detection, and decision-based pipeline optimization. A predictive intelligence layer continuously evaluates pipeline conditions and recommends or initiates adaptive actions, including test prioritization, resource allocation, retry optimization, dependency validation, security-gate adjustment, and execution-path optimization. The framework is designed to reduce pipeline failure rates, improve execution efficiency, minimize recovery time, and strengthen security-aware software delivery. Experimental evaluation can compare predictive models using accuracy, precision, recall, F1-score, ROC-AUC, failure forecasting lead time, pipeline duration, and resource utilization. The study demonstrates how machine learning can transform conventional reactive DevSecOps pipelines into proactive, adaptive, and increasingly autonomous software delivery systems.