Auflistung nach Schlagwort "Testing machine-learning models"
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- KonferenzbeitragProperty-Driven Black-Box Testing of Numeric Functions(Software Engineering 2023, 2023) Sharma, Arnab; Melnikov, Vitalik; Hüllermeier, Eyke; Wehrheim, HeikeIn 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.