Model-Based Testing Without Models: The TodoMVC Case Study.

Web applications define the interface to many of the businesses and services that we interact with and use on a daily basis. The technology stack enabling these applications is constantly changing and applications are accessed from a plethora of different devices. Automated testing of the behavior of applications is a promising strategy for reducing the manual effort that has to be spent on ensuring a consistent user experience across devices. Unfortunately, specifications or models of the desired behavior often do not exist. Model-based testing without models (aka learning-based testing) tries to overcome this hurdle by integrating model learning and model-based testing. In this paper, we sketch the ALEX tool [1, 11] for learning-based testing of web application and demonstrate its operation on benchmarks from the TodoMVC project. Our learning-based conformance analysis reveals that 7 of 27 Todo-apps exhibit behavior that differs from the majority of implementations.