Intelligent CICD Pipeline Optimization Using Machine Learning for Reliable and Continuous Software Delivery

This research developed an intelligent machine-learning-assisted CI/CD pipeline optimization framework to improve the efficiency, reliability, and continuity of software delivery. The framework integrates pipeline data collection, preprocessing and feature engineering, machine-learning analysis, build optimization, intelligent test selection, deployment-risk assessment, failure analysis, and automated rollback within a unified CI/CD architecture. Random Forest, SVM, Gradient Boosting, and XGBoost were evaluated to determine their suitability for data-driven pipeline decision support. The performance analysis demonstrated improvements across the major CI/CD stages. Average build time decreased from 18.6 min to 14.2 min, representing a 23.66% reduction, while test execution time and deployment time were reduced by 32.39% and 18.55%, respectively. Consequently, total pipeline execution time decreased from 62.8 min to 45.8 min, corresponding to an overall reduction of 27.07%. Reliability also improved, with the pipeline success rate increasing from 86.4% to 94.2%, deployment failure rate decreasing from 14.0% to 6.0%, and MTTR decreasing by 42.26%. Among the evaluated machine-learning algorithms, XGBoost achieved the strongest overall classification performance, obtaining 95.2% accuracy, 94.6% precision, 94.1% recall, 94.3% F1-score, and 0.968 ROC-AUC. The results indicate that incorporating historical pipeline behaviour and operational information into CI/CD decision-making can support more efficient build execution, targeted test processing, controlled deployment, and faster failure recovery. The principal contribution of this research is the integration of multiple CI/CD optimization functions within a closed-loop intelligent software delivery framework, rather than addressing build, testing, deployment, and recovery as independent processes. Future work can extend the framework through larger multi-project datasets, additional cloud and containerized environments, model explainability, adaptive model updating, and evaluation across diverse real-world software systems. Overall, the study demonstrates the potential of machine-learning-assisted automation to support faster, more reliable, and resource-efficient continuous software delivery.

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