Cognitive Cloud DevOps Pipeline for Risk-Based Test Automation: A Databricks and SAP-Oracle Integrated Framework for Real-Time Healthcare Software Maintenance
DOI:
https://doi.org/10.15662/g35sd932Keywords:
DevOps pipeline, cloud-native DevOps, risk-based testing, cognitive automation, Databricks, SAP HANA, Oracle Database, healthcare software maintenance, test automation, CI/CDAbstract
In modern healthcare software systems, the need for rapid delivery, regulatory compliance, and high reliability places significant pressure on the software maintenance lifecycle. This paper proposes a cognitive cloud-DevOps pipeline framework that integrates enterprise platforms — specifically Databricks and the combined SAP HANA/Oracle Database ecosystem — to enable risk-based automated testing and real-time software maintenance for healthcare applications. The proposed framework employs machine learning and analytics to drive test-case prioritisation based on risk metrics, integrates CI/CD (continuous integration/continuous deployment) and real-time monitoring in a cloud-native lakehouse architecture, and leverages enterprise data from SAP/Oracle to trigger and optimise test-automation workflows. We demonstrate an instantiation of the framework in a simulated healthcare maintenance scenario, measuring efficiency gains in test-coverage, fault-detection rate, and mean-time-to-repair (MTTR). The results indicate that the risk-based cognitive pipeline achieves faster feedback, reduced redundant test execution, higher defect detection in critical modules, and improved alignment with regulatory constraints compared with conventional scripted automation. The key contributions include (i) a unified architecture combining Databricks analytic pipelines, SAP/Oracle operational data, and DevOps automation; (ii) a cognitive risk-based test automation engine; and (iii) empirical evaluation in a healthcare context. The paper also discusses advantages, trade-offs, limitations and future directions for integrating such pipelines in highly-regulated real-world healthcare environments.
References
1. Basak, S., & Hosain, M. S. (2014). Software testing process model from requirement analysis to maintenance. International Journal of Computer Applications, 107(11), 14-22. doi:10.5120/18795-0147 (ijcaonline.org)
2. Archana, R., & Anand, L. (2023, May). Effective Methods to Detect Liver Cancer Using CNN and Deep Learning Algorithms. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.
3. Sivaraju, P. S. (2024). PRIVATE CLOUD DATABASE CONSOLIDATION IN FINANCIAL SERVICES: A CASE STUDY OF DEUTSCHE BANK APAC MIGRATION. ITEGAM-Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA).
4. Anbalagan, B. (2023). Proactive Failover and Automation Frameworks for Mission-Critical Workloads: Lessons from Manufacturing Industry. International Journal of Research and Applied Innovations, 6(1), 8279-8296.
5. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.
6. Arul Raj .A.M and Sugumar R.,” Monitoring of the social Distance between Passengers in Real-time through video Analytics and Deep learning in Railway stations for Developing highest Efficiency” , March 2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2022, ISBN 979- 835033384-8, March 2023, Chennai , India ., DOI 10.1109/ICDSAAI55433.2022.10028930.
7. Felderer, M., & Schieferdecker, I. (2019). A taxonomy of risk-based testing. Software Quality Journal, (2019). Retrieved from https://arxiv.org/abs/1912.11519 (arxiv.org)
8. Laeeq, S., & Shahid, R. (2014). A survey on risk-based or design-based test cases prioritization. International Journal of Scientific & Engineering Research, 5(12). (ijser.org)
9. Subramanian, K. (2017). Optimizing software releases with risk-based testing. emids Technologies (white paper). (emids.com)
10. Christadoss, J., Sethuraman, S., & Kunju, S. S. (2023). Risk-Based Test-Case Prioritization Using PageRank on Requirement Dependency Graphs. Journal of Artificial Intelligence & Machine Learning Studies, 7, 116-148.
11. Wagner, S., Deissenböck, F., & Zazworka, N. A. (2016). Integrating software quality models into risk-based testing. Software Quality Journal, 26, 809-847. (link.springer.com)
12. Levin, S., & Yehudai, A. (2017). The co-evolution of test maintenance and code maintenance through the lens of fine-grained semantic changes. arXiv. (arxiv.org)
13. AKTER, S., ISLAM, M., FERDOUS, J., HASSAN, M. M., & JABED, M. M. I. (2023). Synergizing Theoretical Foundations and Intelligent Systems: A Unified Approach Through Machine Learning and Artificial Intelligence.
14. Mohammed, N. (2023). The impact of automated testing and DevOps on software quality: A review. International Journal of Core Engineering & Management, 7(05).
15. Shahin, M., Babar, M. A., & Zhu, L. (2017). Continuous integration, delivery and deployment: A systematic review on approaches, tools, challenges and practices. arXiv. (arxiv.org)
16. Ronchieri, E., & Canaparo, M. (2023). Assessing the impact of software quality models in healthcare software systems. Health Systems, 12(1), 85-97. (pmc.ncbi.nlm.nih.gov)
17. Batchu, K. C. (2023). Cross-Platform ETL Federation: A Unified Interface for Multi-Cloud Data Integration. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 9632-9637.
18. Sethupathy, U. K. A. (2023). Zero-touch DevOps: A GenAI-orchestrated SDLC automation framework. World Journal of Advanced Engineering Technology and Sciences, 8(2), 420-433.
19. Johnston, H. (2022, May 4). Implementing AI-enhanced continuous testing in DevOps pipelines: Strategies for automated test generation, execution, and analysis. Blockchain Technology and Distributed Systems. (thesciencebrigade.com)
20. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2024). Artificial Neural Network in Fibre-Reinforced Polymer Composites using ARAS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(2), 9801-9806.
21. Sugumar, R. (2023). Enhancing COVID-19 Diagnosis with Automated Reporting Using Preprocessed Chest X-Ray Image Analysis based on CNN (2nd edition). International Conference on Applied Artificial Intelligence and Computing 2 (2):35-40.
22. Manda, P. (2023). LEVERAGING AI TO IMPROVE PERFORMANCE TUNING IN POST-MIGRATION ORACLE CLOUD ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8714-8725.
23. Thambireddy, S., Bussu, V. R. R., & Mani, R. (2024). Optimizing SAP S/4HANA Upgrades through Sum: The Role of Silent Data Migration (SDMI) in Downtime Reduction. International Journal of Research and Applied Innovations, 7(3), 10727-10734.
24. Kumar, A., Anand, L., & Kannur, A. (2024, November). Optimized Learning Model for Brain-Computer Interface Using Electroencephalogram (EEG) for Neuroprosthetics Robotic Arm Design for Society 5.0. In 2024 International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC) (pp. 30-35). IEEE.
25. Integrate.io. (n.d.). Integrate Databricks with SAP HANA for automated data sync. Retrieved from https://www.integrate.io/integrations/databricks/saphana/ (Integrate.io)
