Enhancing Usability Testing Through A/B Testing, AI-Driven Contextual Testing, and Codeless Automation Tools

Abstract

Background Information: Usability testing is essential for ensuring a seamless user experience in modern applications. Traditional methods often lack scalability and adaptability to real-world scenarios. By integrating A/B testing, AI-driven contextual testing, and codeless automation tools, testing efficiency improves, enabling dynamic UI evaluation, real-world usability assessments, and streamlined automation for comprehensive, data-driven usability testing. Objectives: This study aims to enhance usability testing by leveraging A/B testing for UI optimization, AI- driven contextual testing for real-world adaptation, and codeless automation tools for efficiency. The goal is to increase accuracy, improve test scalability, and streamline the usability evaluation process for faster,
more reliable application development. Methods: A/B testing analyzes multiple UI versions for user engagement effectiveness. AI-driven contextual testing simulates real-world user interactions to detect usability issues dynamically. Codeless automation enables no-code usability test execution, ensuring broader test coverage and increased efficiency while reducing manual intervention in the usability evaluation process. Empirical Results: The proposed method achieved 95% usability issue detection, 96%time efficiency, and
98% scalability. AI-driven contextual testing identified hidden usability flaws, while codeless automation improved resource utilization and testing accuracy, ensuring robust usability assessments. Conclusion: The integration of A/B testing, AI-driven contextual testing, and codeless automation significantly enhances usability testing by improving accuracy, scalability, and test efficiency. Future
enhancements include deep learning-driven UI assessments, blockchain-based test security, and real-time AI-driven usability evaluations for next-generation applications.