Transforming Software Testing in the US: Generative AI Models for Realistic User Simulation

Abstract

Testing software has a higher level of difficulty because of the variations in users’ behaviors, decreasing time of software development, and the demand for prototypical testing. It becomes almost impossible to apply traditional approaches in determining the software’s dynamic environment or its ability to capture different users’ interactions. Introducing to this paper is the hybrid model of Generative AI and RL to model realistic user behaviors whilst modulating to software responses as well. Specifically, in this paper, we discuss the US context by tackling several challenges specific to the regional context of the demographic diversity, the widespread use of Agile/DevOps methodologies and frameworks, and the demand for the highest levels of quality in software testing. The combination of Generative AI for behavior variety with RL for learning makes the given methodology a continuous feedback process for the sake of thorough and realistic behavioral testing. This is well illustrated by real-life applications in areas like e-commerce, healthcare and banking to mention but a few where the model provides robust results terms of identifying difficult to detect faults, test effectiveness and cost benefit analysis. They plan to co-designing federated learning for privacy-preserving testing in the future, as well as leveraging more cross-cultural user simulations that have global application.