AI-Powered Enterprise Quality Engineering through Intelligent Test Automation and Secure Cloud Infrastructure Platforms
DOI:
https://doi.org/10.15662/IJARCST.2024.0705017Keywords:
Artificial Intelligence, Quality Engineering, Intelligent Test Automation, Machine Learning, Cloud Infrastructure, Secure Cloud Computing, Automated Testing, Predictive Defect Analytics, Self-Healing Testing, DevSecOps, Enterprise Software, Continuous TestingAbstract
AI-powered enterprise quality engineering is transforming conventional software testing by integrating artificial intelligence, machine learning, intelligent automation, and secure cloud infrastructure into a unified quality assurance ecosystem. Traditional testing approaches often struggle with complex enterprise applications, rapidly changing requirements, distributed architectures, continuous delivery pipelines, and increasing security risks. This research proposes an AI-powered quality engineering framework that combines intelligent test-case generation, automated regression testing, predictive defect analytics, self-healing test automation, continuous security validation, cloud-based test orchestration, and real-time quality monitoring. The framework uses machine learning models to analyze historical defects, application behavior, code changes, test execution results, and operational telemetry to prioritize testing activities and predict high-risk software components. Secure cloud infrastructure provides scalable environments for parallel test execution while incorporating identity management, encryption, policy enforcement, vulnerability assessment, and continuous compliance monitoring. The proposed methodology follows a structured process involving data collection, preprocessing, AI model development, intelligent test generation, automated execution, security validation, result analysis, and continuous feedback. The framework aims to improve test coverage, defect detection, execution efficiency, release confidence, scalability, and operational resilience while reducing manual testing effort and infrastructure costs. The study demonstrates how AI and cloud technologies can establish a proactive, adaptive, and security-aware quality engineering model for modern enterprise software platforms.
References
1. Karnam, V. S. (2024). Adaptive and federated test automation using AI in distributed multi-cloud and hybrid infrastructure environments. International Journal of Future Innovative Science and Technology (IJFIST), 7(4), 111–121.
2. Chandra, G., Redd, P., & Kadali, R. S. (2022). Lightweight Linux–Powered Edge Systems: Facilitating Secure and Efficient Container Workloads at the Edge. J. Electrical Systems, 18(4), 151-161.
3. Bhagwat, V. B. (2024). A simplified transition from EBS Payroll to Cloud Payroll: Benefits and Drawbacks. Journal of Computational Analysis and Applications, 33(6).
4. Bandaru, P. K. (2021). Leveraging hardware-in-the-loop simulation for continuous automotive software testing. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(5), 3730–3735.
5. Awopejo, T. E., Adigun, P. O., & Oyekanmi, T. T. (2023). Emerging applications of artificial intelligence and machine learning in modern earthquake science. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8142–8154.
6. Gopinathan, V. R. (2023). Intelligent Cloud Security through Continuous Threat Detection and Risk Assessment. International Research Journal of Innovative Engineering, 7(6), 13571-13581.
7. Chaba, A. (2021). API-driven enterprise commerce architecture for composable digital ecosystems. International Journal of Engineering & Extended Technologies Research, 3(6), 4082–4086.
8. Sivakumer, D., & Punithavel, R. K. (2024). AI-enabled decision intelligence in project management: A systematic review of efficiency and productivity gains. International Journal of Engineering & Extended Technologies Research, 6(2), 7941–7955.
9. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
10. Pokala, H. K. (2023). Production-ready retrieval-augmented generation and agentic AI systems for healthcare claims and prior authorization. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 993-1002.
11. Vemireddy, S. (2023). Building resilient enterprise platforms using event-driven and distributed computing models. International Journal of Research and Applied Innovations (IJRAI), 6(6), 10106–10112.
12. Shekhar, P. C. (2022). Accelerating agile quality assurance with AI-powered testing strategies. International Journal of Scientific Research in Engineering and Management, 6(1), 1–11.
13. Gummadi, V. P. K. (2023). MuleSoft batch processing: High-volume streaming architecture. Computer Fraud & Security, 2023(12), 50–57. https://doi.org/10.52710/cfs.886
14. Narapareddy, V. S. R., & Yerramilli, S. K. (2024). Devops Compliance-as-Code. Universal Library of Engineering Technology., 01 (02), 47–54.
15. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
16. Beeram, S. (2024). AI-driven intelligent traffic management in Microsoft Azure: Predictive routing for resilient cloud applications. International Journal of Advanced Engineering Science and Information Technology, 7(4), 14534–14538.
17. Mathew, A. (2023). Sentinel AI: An Investigation into Robust Threat Mitigation Strategies for Artificial Intelligence. Educational Research (IJMCER), 5(5), 108-111.
18. Challa, R. (2023). Engineering AI Factories: Scalable HPC Design Patterns for Next-Generation Foundation Model Infrastructure. International Journal of Computer Technology and Electronics Communication, 6(4), 7342-7351.
19. Anand, L. (2022). Integrating Kubernetes Microservices with Privileged Access Security and Real-Time Fraud Detection for Modern Enterprise Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7453-7461.
20. Bellundagi, M. (2023). Design of an Intelligent Clinical Decision Support System Using Machine Learning Techniques. International Journal of Research and Applied Innovations, 6(6), 10075-10081.
21. Vimal Raja, G. (2024). Intelligent data transition in automotive manufacturing systems using machine learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515-518.
22. Rella, B. P. R. (2022). MLOPs and DataOps integration for scalable machine learning deployment. International Journal for Multidisciplinary Research (Vols. 1–3)[Journal-article]. https://www. researchgate. net/publication/390554912https://www. ijfmr. com/research-paper. php.
23. Hoque, M. J., Akter, F., & Mohammad, A. R. (2019). Data-Driven Decision Support System for Restaurant Business Optimization. American Journal of Economics and Business Management, 2(2).
24. Nisar, K. (2024). Prompting, retrieval, and fine-tuning: Foundations of enterprise language model adaptation. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 9310–9319.
25. Vani, M., & Dadlani, D. (2023). Smart home – “Aashraya” IoT automation system. International Journal of Computer Technology and Electronics Communication, 6(1), 6393–6403.
26. Padmanabham, S. (2022). Secure enterprise architecture for pharmacy benefit management platforms. International Journal of Engineering & Extended Technologies Research, 4(5), 5381–5386.
27. Wadhwa, R. (2023). Optimizing Enterprise Application Performance Through Event-Driven Microservices and Distributed Database Design. Journal ID, 211, 6317.
28. Yepuri, V. K., Polamarasetty, V. K., Donthi, S., & Gondi, A. K. R. (2023). Containerization of a polyglot microservice application using Docker and Kubernetes.arXiv preprint arXiv:2305.00600
29. Abd-Rouf, M. S. K., Adigun, P. O., Alalade, E. O., Oyekanmi, T. T., Faniyi, A. J., Oladapo, B., Awopejo, T. E., Adegoke, O. S., Jamiu, A., Michael, O. B., Obisesan, A., Ajala, S., Adekanye, M. A., Yambali, P. M., & Abd-Rouf, A. B. (2024). From molecular profiling to predictive algorithms: A conceptual machine-learning framework for mechanism-informed therapy selection in multidrug-resistant cancer. International Journal of Science, Research and Technology (IJSRAT), 7(3), 12085–12101.
30. Sugumar, R. (2024). AI-driven cloud framework for real-time financial threat detection in digital banking and SAP environments. International Journal of Technology, Management and Humanities, 10(04), 165-175.
31. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
32. Mathew, A., & Romasco, L. (2024). Forensic Investigation of Artificial Intelligence Systems. Research Updates in Mathematics and Computer Science Vol. 4, 154-164.
33. Gujarathi, M. (2022). Parallel verification architecture for low-latency enterprise decision systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4640-4644.
34. Pasumarthi, H. (2024). AI-driven forecasting and optimization in distributed systems: Lessons from retail, lending, and healthcare platforms. International Journal of Research and Applied Innovations, 7(3), 10786-10790.
35. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
36. Koganti, H. (2023). Architecting high-availability Java microservices for financial applications on AWS. International Journal of Science, Research and Technology, 6(3), 9934–9945.
37. Singh, I. K. (2024). DevSecOps for enterprise ontology platforms: Continuous validation, security, and compliance of semantic knowledge systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(2), 10359-10369.
38. Gujarathi, M. (2023). Zero-Downtime Modernization of Enterprise Platforms: Migration Patterns and Operational Considerations. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8770-8774.
39. Rajula, A. (2024). Drift-aligned personalization for privacy-preserving edge health monitoring. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(5), 8895–8906.
40. Islam, M. S., Shokran, M., & Ferdousi, J. (2024). AI-powered business analytics in marketing: Unlock consumer insights for competitive growth in the US market. Journal of Computer Science and Technology Studies, 6(1), 293-313.
41. Soundappan, S. J. (2023). Designing Intelligent Enterprise Platforms Using Machine Learning Driven API Engineering and Cloud Native Security. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9074-9081.
