Autonomous Cloud Quality Assurance System for Healthcare and Banking A Neural Network–Enhanced Oracle EBS Framework using NLP and Data Mining on Azure DevOps and GitHub
DOI:
https://doi.org/10.15662/IJEETR.2025.0706010Keywords:
cloud quality assurance, neural network, Oracle EBS, NLP, data mining, Azure DevOps, GitHub, healthcare QA, banking QA, autonomous testingAbstract
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.
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