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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. 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Remote evaluation of an interface usability using asynchronous auto-logging methodology. International Journal of Scientific & Technology Research, 4(11), 133–136. https://www.ijstr.org/final-print/nov2015/Remote-Evaluation-Of-An-Interface-Usability-Using-Asynchronous-Auto-logging-Methodology.pdf" }, { "key": "ref8", "doi": "10.14419/ijet.v7i2.8.10438", "unstructured": "Anand, R., & Arulprakash, M. (2018). Business driven automation testing framework. International Journal of Engineering and Technology, 7, 345. https://doi.org/10.14419/IJET.V7I2.8.10438" }, { "key": "ref9", "doi": "10.18535/ijecs/v6i3.40", "unstructured": "Sathyavathy, V. (2017). Evaluation of Software Testing Techniques Using Artificial Neural Network. International Journal of Engineering and Computer Science, 6(3). https://doi.org/10.18535/IJECS/V6I3.40" }, { "key": "ref10", "doi": "10.1080/0144929x.2016.1225818", "unstructured": "Øvad, T., & Larsen, L. B. (2016). 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