Extracting Temporal Models from Data Episodes

The testing objective is to find interactions with a system under test leading to unexpected behavior. Such interactions are test cases that can be either manually specified or automatically generated. For the latter, we find many methods and techniques in the research literature, including combinatorial testing or model-based testing. In this paper, we focus on automated test case generation based on models where we are interested in extracting models from available data. In particular, we consider automotive testing, where cars and other vehicles must behave correctly in typical driving situations. The idea is to use available driving data from which we want to extract driving models that we can later use for generating test cases, i.e., arbitrary driving patterns for vehicle testing. Besides outlining the foundations, we discuss the first experimental results we obtain using available open-access driving data.