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.