Articles Open Access
Cloud-Native DevOps Automation Using Intelligent CI/CD Pipelines for Highly Resilient Enterprise Software Delivery
School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham, United Kingdom
Abstract
Cloud-native software development has transformed enterprise application delivery by emphasizing automation, scalability, continuous integration, continuous deployment, containerization, and distributed infrastructure. However, increasing application complexity, frequent releases, heterogeneous cloud environments, and growing security requirements create challenges for conventional CI/CD pipelines. This study proposes an intelligent cloud-native DevOps automation framework that integrates machine learning, automated testing, predictive failure analysis, security intelligence, container orchestration, and adaptive deployment strategies to improve enterprise software delivery resilience. The proposed framework continuously analyzes source-code changes, build outcomes, test results, infrastructure telemetry, deployment events, and historical pipeline behavior to identify potential failures before production release. Intelligent decision mechanisms dynamically optimize pipeline execution, resource allocation, test prioritization, deployment sequencing, and rollback actions. The architecture integrates automated quality gates, DevSecOps controls, infrastructure-as-code, container orchestration, observability, and policy-driven governance within a unified CI/CD environment. Experimental evaluation considers deployment frequency, lead time, pipeline success rate, failure recovery time, change failure rate, test execution efficiency, resource utilization, security findings, and rollback effectiveness. Comparative analysis with conventional CI/CD pipelines is used to determine the contribution of intelligent automation and predictive analytics. The proposed approach aims to reduce deployment failures, accelerate software delivery, strengthen security, and improve operational resilience. By combining cloud-native engineering practices with intelligent pipeline automation, the framework provides a scalable foundation for reliable, secure, and adaptive enterprise software delivery.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 10 |
| Number | 5 |
| Pages | 19791-19799 |
| Published | 2026-10-13 |
| Issue | Vol. 10 No. 5 (2026): International Research Journal of Innovative Engineering (IRJIE) |
References
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34. Selvarajan, K. (2025). AI-Driven Enterprise Supply Chain Intelligence: A Technical Deep Dive. Journal of Computer Science and Technology Studies, 7(2), 612-617.
35. Nelavala, M. M. (2026). Protocol-Aware Chaos Engineering for SWIFT Gpi-Compatible Microservices: A Framework for Five-Nines Cross-Border Payment Resilience. Journal of Intelligent Decision Making and Information Science, 3(10s), 621-637.
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39. Challa, A., & Konatham, M. R. (2024). Self-Healing CI/CD Pipelines with Feedback-Loop Automation: Building Fault-Tolerant CI/CD Systems Using Anomaly Detection and Automated Rollback Logic. Int. J. Intell. Syst. Appl. Eng, 12(23s), 3217.
2. Reddy, K. V. (2024). Accelerating Functional Coverage Closure Through Iterative Machine Learning. Int. J. Comput. Appl. Inf. Technol, 7, 1401-1411.
3. Gajjela, H., & Kari, M. (2024). Secure Multi-Tenant AI Architecture Enhances Enterprise AI Adoption Through Tenant Trust Using PLS-SEM in India’s Salesforce Cloud Industry. International Journal of Engineering Science & Humanities, 14(2), 253-271.
4. Challa, A., & Konatham, M. R. (2024). Self-Healing CI/CD Pipelines with Feedback-Loop Automation: Building Fault-Tolerant CI/CD Systems Using Anomaly Detection and Automated Rollback Logic. Int. J. Intell. Syst. Appl. Eng, 12(23s), 3217.
5. Polamarasetty, V. K. (2025). Scalable Workday benefits integration frameworks for enterprise HR technology. International Journal of Computer Technology and Electronics Communication, 8(2), 10483–10489.
6. Nabil, M. A., Akand, A. R., Datta, A., Akter, K. S., Hossain, I., & Prince, N. U. (2026, August). A Multi-Dimensional Supervised Machine Learning Framework for Cybersecurity-Focused Social Media Bot Detection. In 2026 7th International Conference On Computational Vision and Bio Inspired Computing (ICCVBIC) (pp. 88-93). IEEE.
7. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
8. Pothuri, M. K. (2025). How modern BI stacks power smarter, faster decision-making in public health care—Blueprint of a modern BI architecture: Designing for scale, speed, and self-service. International Journal for Multidisciplinary Research, 7(5). https://doi.org/10.36948/ijfmr.2025.v07i05.57947
9. Nelavala, M. M. (2026). Protocol-Aware Chaos Engineering for SWIFT Gpi-Compatible Microservices: A Framework for Five-Nines Cross-Border Payment Resilience. Journal of Intelligent Decision Making and Information Science, 3(10s), 621-637.
10. Sandhu, Y. S. (2025). Scalable pipeline orchestration through intelligent dependency-aware recovery and performance optimization. International Journal of Future Innovative Science and Technology (IJFIST), 8(5), 15712–15722.
11. Kasarapu, B. C. (2026). Automated Quality Assurance in Web Applications Using Model-Based Testing and Cypress with AI-Assisted Test Generation. Emerging Innovations Frontiers: Science, Technology, and Social Studies, 14-23.
12. Tarigoppula, S. (2023). Cloud-native Oracle database modernization using intelligent migration frameworks and automated recovery architectures. International Journal of Science, Research and Technology, 6(6), 11132–11144.
13. Umamaheswari, S., Harsha, D., Babu, T. S., Paramasivan, M., Bitragunta, S. L. V., & Padmasuresh, L. (2026, August). IoT-Based Smart Sensor Network for Adventure Park Safety and Management. In 2026 9th International Conference on Circuit, Power & Computing Technologies (ICCPCT) (pp. 1898-1902). IEEE.
14. Hossain, M. S., & Rahaman, M. M. (2026). A Comprehensive Cyber Security Architecture for Government Agencies based on the AITIR Framework. Journal of Intelligent Decision Making and Information Science, 3(9s), 1248-1266.
15. Chundi, V. R. K., Agarwal, V., & Arya, P. (2025, November). Green AI for Sustainable Supply Chains: Challenges in Emerging Economies. In 2025 International Conference on Computational Engineering, Sensing Technology and Management (ICCETM) (pp. 1-5). IEEE.
16. Pochincharla, S., & Devineni, A. (2023). Automated compliance-driven patch management and security hardening in multi-cloud banking infrastructure. International Journal of Advances in Signal and Image Sciences, 57–62.
17. Selvarajan, K. (2025). AI-Driven Enterprise Supply Chain Intelligence: A Technical Deep Dive. Journal of Computer Science and Technology Studies, 7(2), 612-617.
18. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
19. Yasin, M., Akter, S., Khan, M. A. N., Tasnim, M., Sultana, S., Jakir, T., ... & Akter, K. S. (2026). XAI-Enabled Intelligent Detection and Mitigation of Zero-Day Attacks in Healthcare IoT. International Journal of Future Innovative Science and Technology (IJFIST), 9(5), 1807.
20. Ahuja, D. (2026, March). Performance Analysis of a Cloud-Native Web Application Deployed on Kubernetes. In 2026 14th International Symposium on Digital Forensics and Security (ISDFS) (pp. 01-06). IEEE.
21. Rekulapalli, K., Kagga, S. R., Ayyagari, V., Akula, A., & Raj Akula, R. (2026). AI in HRM: Enhancing Workforce Competency and Organizational Effectiveness. Available at SSRN 6762998.
22. Tatavarthi, S., Bikkavolu, V., Gulapalli, S., Koilakonda, R. R., & Divi, V. R. (2026, July). HybridFraudNet: A Dual-Branch CNN-GNN Architecture for Healthcare Insurance Fraud Detection. In 2026 Seventeenth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 917-922). IEEE.
23. Kalakoti, M. K. R. (2025). Provider-agnostic infrastructure as code: A modular framework for secure multi-tenant cloud automation. Journal of international crisis and risk communication research, 188-197.
24. Raja, G. V., & Mali, R. K. (2021). Federated learning frameworks for privacy-preserving artificial intelligence applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(3), 4946-4950.
25. Ramasamy, M. (2025). Transforming industries: The impact of AI-driven network engineering and cloud infrastructure. International Journal of Research and Applied Innovations, 8(3), 13114–13118.
26. Ivanov, T., Shrestha, G., Vemireddy, K., Pyayt, A., & Gubanov, M. (2026, May). Hemolix. extract. v: Llm-based information extraction for documents with ai-based plan selection. In Companion of the International Conference on Management of Data (pp. 54-57).
27. Bandari, U., & Perumal, R. S. (2025). Copilots for self-service BI: A practitioner comparison of generative analytics assistants across enterprise data platforms. International Journal of Engineering & Extended Technologies Research, 7(6), 11312–11322.
28. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
29. Yadavalli, V. R. (2023). Cutover intelligence mesh: A dependency-aware architecture for enterprise cloud ERP migration. International Journal of Future Innovative Science and Technology, 6(4), 11015–11031.
30. Ravichandran, S., & Kandasamy, V. (2025). Optimized Attention Augmented Residual Convolutional Neural Network with Fa-Resnet for Fabric Defect Detection. Journal of Control Engineering and Applied Informatics, 27(4), 3-15.
31. Madabathula, L. (2025). A governance-driven enterprise lakehouse architecture for real-time analytics and machine learning enablement. International Journal of Applied Mathematics, 38(12s), 5022–5033.
32. Aragani, V. M., Maroju, P. K., & Mudunuri, L. N. R. (2024, December). Implementing Non-functional Production Regression Testing with Kubernetes. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 231-244). Singapore: Springer Nature Singapore.
33. Neela, S. (2022). Toward intelligent enterprise integration: Cloud-native middleware design patterns and adaptive stream orchestration architectures for autonomous real-time decisioning. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(2), 154–164.
34. Selvarajan, K. (2025). AI-Driven Enterprise Supply Chain Intelligence: A Technical Deep Dive. Journal of Computer Science and Technology Studies, 7(2), 612-617.
35. Nelavala, M. M. (2026). Protocol-Aware Chaos Engineering for SWIFT Gpi-Compatible Microservices: A Framework for Five-Nines Cross-Border Payment Resilience. Journal of Intelligent Decision Making and Information Science, 3(10s), 621-637.
36. Elliott, S., & Roy, S. (2026, April). Critical Transit Infrastructure in Smart Cities and Urban Air Quality: A Multi-City Seasonal Comparison of Ridership and PM 2.5. In 2026 IEEE Conference on Technologies for Sustainability (SusTech) (pp. 1-8). IEEE.
37. Vaduguru, N. N. (2025). Automating the Capital General Rate Case Filing Process Using SAP HANA: A Digital Transformation Approach for Regulatory Compliance. The USA Journals TAJET, 7(06), 178-192.
38. Gajjela, H., & Kari, M. (2024). Secure Multi-Tenant AI Architecture Enhances Enterprise AI Adoption Through Tenant Trust Using PLS-SEM in India’s Salesforce Cloud Industry. International Journal of Engineering Science & Humanities, 14(2), 253-271.
39. Challa, A., & Konatham, M. R. (2024). Self-Healing CI/CD Pipelines with Feedback-Loop Automation: Building Fault-Tolerant CI/CD Systems Using Anomaly Detection and Automated Rollback Logic. Int. J. Intell. Syst. Appl. Eng, 12(23s), 3217.
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