A FRAMEWORK FOR AI-POWERED PERFORMANCE TEST RESULTS ANALYSIS

Abstract

The evolution of software systems has made performance testing increasingly complex, necessitating more sophisticated approaches to results analysis. This article presents a comprehensive framework for leveraging artificial intelligence and machine learning in performance test results analysis, addressing the challenges of traditional manual methods. Various AI/ML techniques, including anomaly detection for identifying performance deviations, predictive modeling for forecasting system behavior, and automated root cause analysis through correlation and classification. The framework incorporates clustering algorithms for understanding load patterns and user behavior, regression analysis for bottleneck identification, and natural language processing for automated test result summarization. This article demonstrates significant improvements in analysis accuracy and efficiency, enabling teams to identify potential performance issues earlier in the development lifecycle while optimizing infrastructure costs. The proposed methodology enhances the reliability of performance testing outcomes and provides actionable insights for proactive system optimization, ultimately contributing to more robust and scalable software systems.