Ai-Driven Automated Testing for Policy and Claims Management in Guidewire

Aim: To examine the impact of AI-driven automated testing on the quality assurance of PolicyCenter and ClaimCenter modules in Guidewire applications, focusing on the reduction of manual testing efforts and improvement in defect detection. Study Design: A quasi-experimental design was used to evaluate the AI-enhanced automated testing framework. The study compared pre- and post-implementation performance metrics, including defect density, testing speed, and code coverage. Place and Duration of Study: This study was conducted at Global Insurance Systems over a 12-week period from March to June 2024, involving the PolicyCenter and ClaimCenter applications. Methodology: The methodology for AI-driven automated testing in Guidewire focuses on streamlining testing for PolicyCenter, BillingCenter, and ClaimCenter. By leveraging AI, the approach automates test case generation, execution, and defect prediction, addressing manual inefficiencies and complex workflows. Natural Language Processing (NLP) parses requirements and scenarios, while Machine Learning (ML) predicts outcomes and prioritizes test cases. Integration with Guidewire modules enables seamless interaction through APIs, and reinforcement learning ensures dynamic adaptation. Real-time monitoring tracks metrics, while automated reporting provides actionable insights. Continuous learning ensures scalability and precision. Conclusion: AI-driven automated testing proved to be highly effective in improving the testing efficiency and defect detection rates for Guidewire applications. By automating routine tasks and leveraging predictive models, the approach not only reduced manual efforts but also enhanced the overall quality of the software. This study highlights the value of AI in modernizing testing practices for enterprise-level applications like Guidewire, contributing to faster releases and better software quality.