  Neurosymbolic artificial intelligence (AI) is an emerging branch of AI that
combines the strengths of symbolic AI and sub-symbolic AI. A major drawback of
sub-symbolic AI is that it acts as a "black box", meaning that predictions are
difficult to explain, making the testing & evaluation (T&E) and validation &
verification (V&V) processes of a system that uses sub-symbolic AI a challenge.
Since neurosymbolic AI combines the advantages of both symbolic and
sub-symbolic AI, this survey explores how neurosymbolic applications can ease
the V&V process. This survey considers two taxonomies of neurosymbolic AI,
evaluates them, and analyzes which algorithms are commonly used as the symbolic
and sub-symbolic components in current applications. Additionally, an overview
of current techniques for the T&E and V&V processes of these components is
provided. Furthermore, it is investigated how the symbolic part is used for T&E
and V&V purposes in current neurosymbolic applications. Our research shows that
neurosymbolic AI as great potential to ease the T&E and V&V processes of
sub-symbolic AI by leveraging the possibilities of symbolic AI. Additionally,
the applicability of current T&E and V&V methods to neurosymbolic AI is
assessed, and how different neurosymbolic architectures can impact these
methods is explored. It is found that current T&E and V&V techniques are partly
sufficient to test, evaluate, verify, or validate the symbolic and sub-symbolic
part of neurosymbolic applications independently, while some of them use
approaches where current T&E and V&V methods are not applicable by default, and
adjustments or even new approaches are needed. Our research shows that there is
great potential in using symbolic AI to test, evaluate, verify, or validate the
predictions of a sub-symbolic model, making neurosymbolic AI an interesting
research direction for safe, secure, and trustworthy AI.
