Abstract

Most communication with real-life systems involves data values being relevant to the communication context and thus influencing the observable behavior of the communication endpoints. When applying methods from the realm of automata learning, it is necessary to handle such data-occurrences. In this paper, we consider how the techniques of automata learning can be adapted to the problem of learning interaction models in which data parameters are an essential element. Especially, we will focus on how test-drivers for real-word systems can be generated automatically. Our main contribution is an analysis of (1) the requirements on information contained in models produced by the learning enabler in the Connect project and (2) the resulting preconditions for generating test-drivers automatically.