session
About the Talk
In the dynamic field of software development, maintaining UI consistency through visual regression testing is crucial. However, traditional methods often face challenges due to their complexity and time constraints. With over 10 years of experience in software testing and quality engineering and a deep understanding of implementing generative AI in the quality engineering domain, I introduce an innovative approach using multi-modal generative AI.
Users can upload images of web pages or UI components, allowing generative AI to extract structured data from visual representations. This method surpasses typical test automation, offering a more efficient, accurate, and scalable solution. The generative AI's ability to process screenshots not only streamlines data validation but also enhances design change detection. In cases of modifications, multi-modal generative capabilities dynamically update and regenerate test cases, ensuring alignment with the latest design elements.
This presentation will not only be theoretical; it will also include a live demo, showcasing the practical application of this cutting-edge technology. Attendees will gain insights into the transformative potential of multi-modal generative AI in visual regression testing, envisioning a future where AI is integral to quality assurance and development.
Key Takeaways:
Ahmed is a seasoned software QA/Test engineer with over a decade of experience in the field. His expertise spans the entire system development life-cycle, encompassing the creation and execution of test plans, test cases, and streamlined test processes. With a strong emphasis on automated testing, Ahmed brings proficiency in various programming languages, enabling him to drive efficiency and quality across projects. As a speaker, Ahmed shares practical insights derived from his extensive background, empowering audiences to optimize QA practices and leverage automation for enhanced software quality and expedited delivery.
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