AI-Powered Test Optimization for Guidewire Applications |
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| © 2025 by IJCTT Journal | ||
| Volume-73 Issue-1 |
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| Year of Publication : 2025 | ||
| Authors : Pavan Kumar Gollapudi | ||
| DOI : 10.14445/22312803/IJCTT-V73I1P107 | ||
How to Cite?
Pavan Kumar Gollapudi, "AI-Powered Test Optimization for Guidewire Applications," International Journal of Computer Trends and Technology, vol. 73, no. 1, pp. 57-64, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I1P107
Abstract
AI is pioneering the future of software testing. It is now possible to achieve unprecedented efficiency and precision in the testing of complex systems such as the Guidewire Insurance Suite. AI-powered test optimization provides machine learning and predictive analytics to streamline quality assurance processes, especially for Guidewire modules like PolicyCenter, ClaimCenter, and BillingCenter. AI identifies high-risk areas by analysing historical data, code changes, and usage patterns and prioritizes the test cases to enable focused defect detection, reducing redundant testing efforts. This will be particularly important for complex workflows and integrations with third-party systems in Guidewire. AI is improving regression testing through test case selection, wherein it chooses only the most relevant cases, reducing execution time while maintaining comprehensive coverage. It also introduces the capabilities of self-healing test scripts, which automatically detect changes in an application's structure or behaviour and adjust themselves without requiring manual intervention. This ensures the continued reliability of automated testing frameworks, even in dynamic and frequently updated environments, by reducing maintenance overhead. Additionally, risk-based testing leverages AI to assess the impact of application changes and prioritize testing efforts on high-risk or business-critical functionalities. This approach minimizes the chances of system failures, ensures the alignment of QA efforts with business priorities, and reduces disruption to operations. Predictive analysis of data is an extension of system reliability and availability. It has a unique ability to pick up potential difficulties in software problems proactively.
Keywords
Artificial Intelligence (AI), Software testing, Guidewire insurance suite, AI-powered test optimization, Quality Assurance (QA), PolicyCenter, ClaimCenter.
Reference
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