Model-based software testing via incremental treatment learning

Model-based software has become quite popular in recent years, making its way into a broad range of areas, including the aerospace industry. The models provide an easy graphical interface to develop systems, which can generate the sometimes tedious code that follows. While there are many tools available to assess standard procedural code, there are limits to the testing of model-based systems. A major problem with the models are that their internals often contain gray areas of unknown system behavior. These possible behaviors form what is known as a data cloud, which is an overwhelming range of possibilities of a system that can overload analysts (Menzies et al., 2003). With large data clouds, it is hard to demonstrate which particular decision leads to a particular outcome. Even if definite decisions can't be made, it is possible to reduce the variance of and condense the clouds (Menzies et al., 2003). This paper presents two case studies; one with a simple illustrative model and another with a more complex application. The TAR3 treatment learning tool summarizes the particular attribute ranges that selects for particular behaviors of interest, reducing the data clouds.