GIST: Generated Inputs Sets Transferability in Deep Learning

As the demand for verifiability and testability of neural networks continues to rise, an increasing number of methods for generating test sets are being developed. However, each of these techniques tends to emphasize specific testing aspects and can be quite time-consuming. A straightforward solution to mitigate this issue is to transfer test sets between some benchmarked models and a new model under test, based on a desirable property one wishes to transfer. This paper introduces GIST (Generated Inputs Sets Transferability), a novel approach for the efficient transfer of test sets among Deep Learning models. Given a property of interest that a user wishes to transfer (e.g., coverage criterion), GIST enables the selection of good test sets from the point of view of this property among available ones from a benchmark. We empirically evaluate GIST on fault types coverage property with two modalities and different test set generation procedures to demonstrate the approach's feasibility. Experimental results show that GIST can select an effective test set for the given property to transfer it to the model under test. Our results suggest that GIST could be applied to transfer other properties and could generalize to different test sets' generation procedures and modalities