(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2025)
In the era of digital transformation, both healthcare and banking sectors are adopting agile, cloud-native and DevOps-centric methods, which impose heightened demands on quality assurance (QA). This research proposes an autonomous cloud QA system that integrates neural-network-based defect prediction, natural language processing (NLP) of requirements and change-logs, and data-mining of version history within the context of Oracle E‑Business Suite (EBS) implementations. The system is deployed on the Azure DevOps platform and leverages GitHub for source-control analytics. For both healthcare and banking domains — with their stringent regulatory, data integrity, and continuity requirements — this framework offers automated extraction of test-cases from deployment pipelines, prioritisation of high-risk modules via neural networks, and continuous validation of functional, non-functional and compliance requirements. A prototype was implemented and evaluated in two pilot settings: a healthcare provider using EBS modules for patient administration and billing, and a bank deploying EBS for core-ledger and payments. Results show significant reduction in post-release defects (approx. 30 %) and accelerated release-cycles (approx. 25 % faster) while maintaining regulatory audit traceability. Key advantages include proactive defect prediction, full traceability from requirement to test to production, and domain-specific configurability (healthcare vs banking). Limitations include initial model-training overhead, domain-specific rule-customisation and dependence on high-quality historical data. Future work will explore expanded domain coverage (insurance, life-sciences), inclusion of federated learning for cross-organisation knowledge sharing, and real-time anomaly detection in live production.