Smart Human-Centric Ecosystems (SHCS) are an important class of systems that impact on our everyday life. They span from smart homes to smart cities, smart grids, autonomous vehicles, smart schools, smart healthcare systems. SHCS comprises a wide range of autonomous and heterogeneous hardware and software systems that interact explicitly and implicitly in a shared environment. The core elements of SHCS are humans. Humans interact both directly and indirectly with the systems in the SHCS: Simply standing, walking, running, or jumping in a smart city can impact on the behavior of the smart city SHCS. This complete freedom of human actions and the vast variety of interactions make it challenging to test SHCS. We hypothesize that sequences of human actions in SHCS depends on the personality, and this relation help us automatically generate test cases for SHCS. We assume that it is possible to infer the personality of humans by observing the human actions in the SHCS up to a given instant, and use this information to infer the most likely human actions that can follow. We use this information to automatically generate test sequences for SHCS. With the use of personality models, we can inject new personalities in the SHCS, to test how different personalities and social groups interact in the context of new scenarios and system behavior before they happen in the field.