Machine learning-driven root cause analysis and predictive defect prevention in enterprise insurance software
Department of Software Development P and C., Golden Bear Insurance Company, Prosper, TX, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(02), 2133-2145
Article DOI: 10.30574/wjarr.2024.21.2.0485
Publication history:
Received on 02 January 2024; revised on 22 February 2024; accepted on 27 February 2024
Abstract:
Enterprise insurance software systems exhibit complex failure patterns that traditional root cause analysis methods struggle to address effectively, leading to recurring defects and prolonged resolution cycles. This research presents a comprehensive machine learning-driven framework for automated root cause analysis and predictive defect prevention specifically designed for large-scale insurance applications. The proposed system integrates multiple machine learning algorithms including association rule mining, clustering techniques, and deep neural networks to automatically analyze defect patterns, system logs, and code repository data to identify underlying root causes and predict potential future failures. Our methodology combines supervised learning for known defect pattern recognition with unsupervised learning for discovering novel failure modes and their correlations across different system components. The framework utilizes natural language processing techniques to analyze defect descriptions, support tickets, and system documentation to extract semantic relationships between failures and their contextual factors. Implementation across multiple Guidewire implementation projects demonstrates significant improvements in defect resolution efficiency: 54% reduction in average resolution time, 67% decrease in recurring defects, and 43% improvement in first-time fix rates. The system incorporates advanced feature engineering techniques to extract meaningful patterns from diverse data sources including code complexity metrics, test coverage statistics, deployment configurations, and environmental factors. Graph neural networks analyze complex dependencies between software components, infrastructure elements, and business processes to identify cascading failure scenarios and their prevention strategies. The research contributes novel algorithms for multi-dimensional defect clustering that considers technical, functional, and business impact factors to prioritize remediation efforts effectively. Real-time monitoring capabilities enable proactive defect prevention through early warning systems that alert development teams to emerging risk patterns before they manifest as production issues. Experimental validation shows 72% accuracy in predicting defects 2-3 sprints before their occurrence, enabling proactive mitigation strategies. The proposed solution integrates with popular development tools including Azure DevOps, JIRA, and GitHub to provide seamless workflow integration and actionable insights for continuous quality improvement in enterprise insurance software development lifecycles.
Keywords:
Root Cause Analysis; Predictive Defect Prevention; Machine Learning; Insurance Software; Software Quality; Enterprise Applications; Automated Testing
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0