LEVERAGING ARTIFICIAL INTELLIGENCEAND MACHINE LEARNING FOR ENHANCED TEST AUTOMATION
Keywords:
AI/ML in Test Automation, Test Case Selection and Prioritization, Dynamic Test Case Generation, Adaptation, Test Execution Optimization, AIPowered Test AnalysisAbstract
The integration of artificial intelligence (AI) and machine learning (ML) techniques into test automation frameworks has emerged as a promising approach to enhance software testing processes. This paper explores the potential of AI and ML in augmenting traditional test automation approaches, focusing on test case selection, execution, and analysis. By leveraging data-driven insights and predictive analytics, AI/ML algorithms can intelligently prioritize test cases, facilitate dynamic test case generation, optimize test execution, and provide deeper insights into test results and defect patterns. Through case studies and empirical evaluations, this research aims to provide practical guidelines for integrating AI/ML into test automation frameworks, enabling organizations to improve software quality, accelerate time-to-market, and drive continuous innovation in software development processes.
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Copyright (c) 2024 Srinivasa Rao Vemula (Author)
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