Sharma, ArnabMelnikov, VitalikHüllermeier, EykeWehrheim, HeikeEngels, GregorHebig, ReginaTichy, Matthias2023-01-182023-01-182023978-3-88579-726-5https://dl.gi.de/handle/20.500.12116/40109In this work, we propose a property-driven testing mechanism to perform unit testing of functions performing numerical computations. Our approach, similar to the property-based testing technique, allows the tester to specify the requirements to check. Unlike property-based testing, the specification is then used to generate test cases in a targeted manner. Moreover, our approach works as a black-box testing tool, i.e. it does not require knowledge about the internals of the function under test. Therefore, besides on programmed numeric functions, we also apply our technique to machine-learned regression models. The experimental evaluation on a number of case studies shows the effectiveness of our testing approach.enProperty-based testingRegressionTesting machine-learning modelsText/Conference Paper1617-5468