In today’s rapidly evolving software development landscape, DevOps has emerged as a vital practice to ensure faster delivery and improved collabora- tion between development and operations teams. Jenkins, a widely adopted open-source automation server, is central to Continuous Integration and Con- tinuous Deployment (CI/CD) pipelines. However, as systems scale, tradi- tional methods of pipeline optimization often fall short in maintaining per- formance and resource efficiency. This paper explores the integration of Ar- tificial Intelligence (AI) to dynamically enhance the performance of Jenkins pipelines in scalable DevOps environments.

We propose an AI-driven framework that leverages machine learning mod- els to analyze historical build data, identify pipeline bottlenecks, predict build failures, and automatically tune performance parameters. Techniques such as anomaly detection, reinforcement learning, and predictive analytics are employed to provide actionable insights and automation in decision-making. The framework monitors pipeline execution metrics—such as queue times, ex- ecutor utilization, and test runtimes—and learns optimal configurations that minimize latency and resource overhead. The AI models continuously adapt to evolving workloads, ensuring that pipelines remain efficient as project de- mands change.

To validate the proposed framework, we conducted experiments using real-world Jenkins job data from enterprise-scale DevOps environments. Re- sults show a 30–45 This study demonstrates that AI can play a transformative role in op- timizing CI/CD workflows by introducing intelligence, adaptability, and re- silience into pipeline management. Our solution provides a generalized ap- proach that can be extended to other orchestration tools and aligns with the broader goals of DevOps automation and intelligent software engineer- ing. Ultimately, the research contributes to the growing field of AIOps by showcasing how AI-enhanced automation in DevOps environments can lead to higher software delivery velocity, improved developer productivity, and re- duced operational costs. Future work includes expanding the framework to support cross-platform integration, real-time observability dashboards, and federated learning for multi-tenant DevOps ecosystems.

Artificial Intelligence, Jenkins Pipelines, Performance Tuning, DevOps, CI/CD, Machine, Learning, Predictive Analytics, AIOps, Pipeline Optimiza- tion, Build Automation, Cloud Computing, Containerization, Docker, Ku- bernetes, Automated Testing

Debois, P. (2009). DevOps: A software revolution in the making. Cutter IT Journal, 22(12), 34–39.

Humble, J., & Farley, D. (2010). Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. Addison- Wesley.

Shahin, M., Babar, M. A., & Zhu, L. (2017). Continuous integration, de- livery and deployment: A systematic review. IEEE Access, 5, 3909–3943.

Kim, G., Humble, J., Debois, P., & Willis, J. (2016). The DevOps Hand- book. IT Revolution.

Bass, L., Weber, I., & Zhu, L. (2015). DevOps: A Software Architect’s Perspective. Addison-Wesley.

Ghaleb, T., & Galster, M. (2021). Applying machine learning techniques to enhance CI/CD pipelines: A survey. IEEE Software, 38(5), 47–54.

Erich, F. M., Amrit, C., & Daneva, M. (2017). DevOps literature review. Proceedings of the International Conference on Software and System Process, 165–169.

Duraes, J., Madeira, H., et al. (2020). Using machine learning for in- telligent failure prediction in CI/CD pipelines. Journal of Systems and Software, 169, 110710.

Sykiotis, D., et al. (2021). Anomaly detection for continuous integration pipelines using unsupervised learning. ACM Transactions on Software Engineering and Methodology, 30(4), 1–34.

Jabbari, R., Ali, N., Petersen, K., & Tanveer, B. (2016). What is De- vOps? A systematic mapping study on definitions and practices. Pro- ceedings of the Scientific Workshop Proceedings of XP, 1–11.