Cloud-Native Risk-Based Testing Pipeline for Healthcare ERP Systems A Databricks-Driven Intelligence Model for SAP and Oracle EBS Workloads
Main Article Content
Abstract
This paper proposes a novel cloud-native, risk-based testing pipeline for healthcare enterprise resource planning (ERP) systems, specifically targeting the integration of SAP S/4HANA (and legacy SAP) and Oracle E‑Business Suite (Oracle EBS) workloads within real-time and mission-critical healthcare environments. Our approach leverages the data engineering and intelligence capabilities of the Databricks Lakehouse platform to drive test orchestration, risk scoring, and continuous validation. In healthcare settings, ERP systems are increasingly integrated with clinical and operational data streams—medical device telemetry, patient-monitoring systems, staffing and supply‐chain systems—so testing pipelines must be agile, responsive and risk-aware. The proposed pipeline incorporates phases of ingestion of ERP data (transactional, configuration, customisation), risk profiling (based on business process criticality, compliance sensitivity, change impact), test generation (automated selection of high-risk scenarios), execution (cloud‐native test harnesses, containerised test runners, orchestration via CI/CD), monitoring (real-time telemetry of test outcomes and risk metrics) and feedback loops (to adjust risk weights and test scope). A case study simulation is presented for a large healthcare provider migrating SAP and Oracle EBS workloads into a multicloud environment, showing improvements in defect detection in high-risk modules, reduction in testing cycle time and enhanced compliance traceability. The results show that coupling Databricks-driven intelligence with a risk-based testing framework yields a testing pipeline that is scalable, adaptive, and aligned with healthcare-specific regulatory, operational and data-quality demands. The paper closes by discussing benefits, limitations and suggestions for future work in healthcare ERP testing pipelines.
Article Details
Section
How to Cite
References
1. Kunz, I., Schneider, A., & Banse, C. (2022). A continuous risk assessment methodology for cloud infrastructures. arXiv.
2. Gosangi, S. R. (2023). AI AND THE FUTURE OF PUBLIC SECTOR ERP: INTELLIGENT AUTOMATION BEYOND DATA ANALYTICS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 8991-8995.
3. 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.
4. Sugumar R., et.al IMPROVED PARTICLE SWARM OPTIMIZATION WITH DEEP LEARNING-BASED MUNICIPAL SOLID WASTE MANAGEMENT IN SMART CITIES, Revista de Gestao Social e Ambiental, V-17, I-4, 2023.
5. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
6. Pranto, M. R. H., Zerine, I., Islam, M. M., Akter, M., & Rahman, T. (2023). Detecting Tax Evasion and Financial Crimes in The United States Using Advanced Data Mining Technique. Business and Social Sciences, 1(1), 1-11.
7. Ahmed, I., Shahin, M., & Ali, N. (2020). DevOps and software quality: A systematic mapping. Journal of Systems and Software, 171(1), 110817. https://doi.org/10.1016/j.jss.2020.110817
8. Basu, A., & Kumar, R. (2021). Cloud-native testing frameworks: Modern practices for continuous quality assurance. IEEE Access, 9, 13452–13467. https://doi.org/10.1109/ACCESS.2021.3050273
9. Boehm, B., & Basili, V. (2011). Software defect reduction top 10 list revisited. IEEE Computer, 44(4), 87–91. https://doi.org/10.1109/MC.2011.128
10. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2021). The evolution of software maintenance. Journal of Computer Science Applications and Information Technology, 6(1), 1–8. https://doi.org/10.15226/2474-9257/6/1/00150
11. Anugula Sethupathy, U.K. (2022). API-driven architectures for modern digital payment and virtual account systems. International Research Journal of Modernization in Engineering Technology and Science, 4(8), 2442–2451. https://doi.org/10.56726/IRJMETS29156
12. 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.
13. Batchu, K. C. (2022). Serverless ETL with Auto-Scaling Triggers: A Performance-Driven Design on AWS Lambda and Step Functions. International Journal of Computer Technology and Electronics Communication, 5(3), 5122-5131.
14. Jhawar, R., & Piuri, V. (2012). Fault tolerance and resilience in cloud computing environments. IEEE Symposium on High Assurance Systems Engineering, 185–190. https://doi.org/10.1109/HASE.2012.17
15. Kim, S., Yoo, C., & Lee, Y. (2019). Data-driven risk-based testing approach using machine learning for enterprise systems. Journal of Software: Evolution and Process, 31(11), e2191. https://doi.org/10.1002/smr.2191
16. Kadar, Mohamed Abdul. "MEDAI-GUARD: An Intelligent Software Engineering Framework for Real-time Patient Monitoring Systems." (2019).
17. 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.
18. Panaya Ltd. (2018). Risk-based testing for SAP: Save time without sacrificing quality. Panaya White Paper. https://www.panaya.com/blog/testing/risk-based-testing-save-time-without-sacrificing-quality/
19. Rangarajan, S., Liu, H., Wang, H., & Wang, C.-L. (2018). Scalable architecture for personalized healthcare service recommendation using big-data lake. arXiv Preprint, arXiv:1802.04105. https://arxiv.org/abs/1802.04105
20. Shatnawi, A., Orrù, M., Mobilio, M., Riganelli, O., & Mariani, L. (2018). CloudHealth: A model-driven approach to watch the health of cloud services. arXiv Preprint, arXiv:1803.05233. https://arxiv.org/abs/1803.05233
21. Sivaraju, P. S., & Mani, R. (2024). Private Cloud Database Consolidation in Financial Services: A Comprehensive Case Study on APAC Financial Industry Migration and Modernization Initiatives. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10472-10490.
22. Vijajakumar, R., & Arun, P. (2020). Cloud-based business process security risk management: A systematic review, taxonomy, and future directions. Computers, 9(12), 160. https://doi.org/10.3390/computers9120160
23. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2020). Applying design methodology to software development using WPM method. Journal ofComputer Science Applications and Information Technology, 5(1), 1-8.
24. Arulraj AM, Sugumar, R., Estimating social distance in public places for COVID-19 protocol using region CNN, Indonesian Journal of Electrical Engineering and Computer Science, 30(1), pp.414-424, April 2023.
25. Vinay, T. M., Sunil, M., & Anand, L. (2024, April). IoTRACK: An IoT based'Real-Time'Orbiting Satellite Tracking System. In 2024 2nd International Conference on Networking and Communications (ICNWC) (pp. 1-6). IEEE.
26. Weng, J., & Zhang, H. (2022). Machine learning-based anomaly detection for SAP ERP transaction logs in cloud environments. ACM Transactions on Management Information Systems, 13(3), 25–42. https://doi.org/10.1145/3524123