GIST: Generated Inputs Sets Transferability in Deep Learning
Florian Tambon Foutse Khomh Giuliano Antoniol
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