Published Paper Details:

Software testing plays a crucial role in ensuring the quality, reliability, and performance of modern software applications. Traditional testing tools, such as Selenium, rely heavily on predefined scripts and manual intervention, which often leads to increased maintenance effort, longer execution time, and limited adaptability to dynamic application changes. These limitations become more significant as software systems grow in complexity and scale. To address these challenges, AI-based testing approaches have emerged as a promising solution. AI-driven tools like Testim utilize machine learning algorithms and intelligent automation to enhance testing processes. These tools are capable of automatically generating test cases, adapting to user interface changes through self-healing mechanisms, and predicting potential defects based on historical data. This research presents a comparative performance evaluation of traditional testing tools and AI-based testing approaches. The study employs a structured methodology involving controlled experiments and analysis of key performance metrics such as execution time, defect detection rate, and maintenance effort. The results indicate that AI-based testing significantly improves efficiency, reduces manual intervention, and enhances overall testing accuracy. The findings suggest that integrating AI into software testing processes can lead to more scalable, reliable, and efficient testing solutions, making it a valuable approach for modern software development environments.