GIST: Generated Inputs Sets Transferability in Deep Learning

Replication Package for the paper "GIST: Generated Inputs Sets Transferability in Deep Learning"

Contains most of the data, models ... used

Github link: https://github.com/FlowSs/GIST

Part2 of the replication package can be found here: https://zenodo.org/records/10839634

Abstract:

   To foster the verifiability and testability of Deep Neural Networks (DNN), an increasing number of methods
for test case generation techniques are being developed.
    When confronted with testing DNN models, the user can apply any existing test generation technique.
However, it needs to do so for each technique and each DNN model under test, which can be expensive.
Therefore, a paradigm shift could benefit this testing process: rather than regenerating the test set independently
for each DNN model under test, we could transfer from existing DNN models.
     This paper introduces GIST (Generated Inputs Sets Transferability), a novel approach for the efficient
transfer of test sets. Given a property selected by a user (e.g., neurons covered, faults), GIST enables the
selection of good test sets from the point of view of this property among available test sets. This allows the
user to recover similar properties on the transferred test sets as he would have obtained by generating the
test set from scratch with a test cases generation technique. Experimental results show that GIST can select
effective test sets for the given property to transfer. Moreover, GIST scales better than reapplying test case
generation techniques from scratch on DNN models under test.