  The input domain of software systems can typically be divided into
sub-domains for which the outputs are similar. To ensure high quality it is
critical to test the software on the boundaries between these sub-domains.
Consequently, boundary value analysis and testing has been part of the toolbox
of software testers for long and is typically taught early to students.
However, despite its many argued benefits, boundary value analysis for a given
specification or piece of software is typically described in abstract terms
which allow for variation in how testers apply it.
  Here we propose an automated, black-box boundary value detection method to
support software testers in systematic boundary value analysis with consistent
results. The method builds on a metric to quantify the level of boundariness of
test inputs: the program derivative. By coupling it with search algorithms we
find and rank pairs of inputs as good boundary candidates, i.e. inputs close
together but with outputs far apart. We implement our AutoBVA approach and
evaluate it on a curated dataset of example programs. Our results indicate that
even with a simple and generic program derivative variant in combination with
broad sampling over the input space, interesting boundary candidates can be
identified.
