AI-Driven Test Case Optimization: Enhancing Efficiency in Software Testing Life Cycle

As the development of software products grows more intricate, creative ways must be found to guarantee the adequacy and efficiency of the testing. AI has become one the most effective and popular techniques in software testing specifically to the test case generation and optimization. This paper focuses on the use of artificial intelligence which spans from machine learning, deep learning and natural language processing in enhancing software testing life cycle. Organizations may significantly reduce test coverage, effort, and cost associated with manual testing while simultaneously increasing software quality by implementing AI-based tools. The paper also discusses the issues specific to integrating test case generation based on artificial intelligence and machine learning including the issue of data quality and the integration of the algorithm with current test cases, and lastly the issue of expertise.