International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Cloud-Native AI-Driven Test Automation Framework for Insurance Software Systems

Author(s) Pavan Kumar Gollapudi
Country United States
Abstract Traditional software testing approaches in the insurance domain face significant challenges when dealing with complex, multi-layered systems like Guidewire InsuranceSuite. This paper presents a novel cloud-native artificial intelligence-driven test automation framework specifically designed for insurance software systems. The proposed framework leverages machine learning algorithms to intelligently identify test scenarios, predict defect-prone areas, and optimize test execution sequences. Our approach integrates deep learning models trained on historical defect data, test execution patterns, and business workflow complexities to achieve autonomous test case generation and maintenance. The framework utilizes containerized microservices architecture deployed on AWS/Azure cloud platforms, enabling elastic scaling and cost optimization. Implementation results from two major Guidewire ClaimCenter cloud deployments demonstrate a 67% reduction in test creation time, 45% improvement in defect detection accuracy, and 52% decrease in overall testing costs. The system incorporates reinforcement learning algorithms to continuously adapt test strategies based on application changes and emerging failure patterns. Performance evaluation across 15 insurance domain applications shows superior accuracy compared to traditional rule-based automation frameworks, with precision rates exceeding 92% in critical business workflow validation. The proposed solution addresses key challenges in insurance software testing including complex business rule validation, regulatory compliance verification, and multi-system integration testing while maintaining high reliability and scalability in cloud environments.
Keywords Software Testing, Artificial Intelligence, Machine Learning, Insurance Software, Cloud Computing, Test Automation, Guidewire, Deep Learning.
Field Engineering
Published In Volume 5, Issue 3, May-June 2023
Published On 2023-05-05
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E-ISSN 2582-2160
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