Human Experience Testing – Redefining Quality Engineering in the Age of AI

Artificial Intelligence is rapidly becoming an accepted means of accelerating software engineering and testing activities.  Many organizations have already integrated AI into their software development lifecycle, and the demand for AI-assisted engineering continues to grow year over year.  A key motivating factor behind the demand is the promise of increased speed and efficiency through automation. Software testing is a prime candidate for AI acceleration due to the generally high costs and significant manual effort involved in validation and verification.  As a result, we are seeing an exponential growth in the application and development of tools to support AI-driven test automation. 

But what does the adoption of AI-driven automation mean for the future of software testing?  Is AI going to replace manual testers and/or test automation engineers? It seems like these questions are quickly growing old and repetitive because no one is providing definitive answers based on practical, real-world analysis. Join industry expert Tariq King as he takes you on a journey into the future of software testing. Tariq firmly believes that AI will take over software testing and permanently transform the quality engineering landscape. He postulates that the future of testing will be focused on evaluating human experiences, including testing digital, physical, and experiential touchpoints. In the age of AI, human experience testing will not just be viewed as a quality assurance function, but as a core component of an organization’s business strategy. Welcome to the future of testing, where AI tests software and humans do the one thing the machines can’t – judge human experiences firsthand.

Although there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security, and operations.  Join Tariq King as he gives you a quality engineering introduction to testing AI and machine learning. You’ll learn AI and ML fundamentals, including how intelligent agents are modeled, trained and developed. Tariq then dives into approaches for validating ML models offline, prior to release, and online, continuously post-deployment. Engage with other participants to develop and execute a test plan for a live ML-based recommendation system, and experience the practical issues around testing AI first-hand. Tariq wraps up the tutorial with a set of expert-recommended, AI engineering practices to help your organization develop trusted machine learning systems.