  There has been a growing need to devise processes that can create
comprehensive datasets in the world of Computer Algebra, both for accurate
benchmarking and for new intersections with machine learning technology. We
present here a method to generate integrands that are guaranteed to be
integrable, dubbed the LIOUVILLE method. It is based on Liouville's theorem and
the Parallel Risch Algorithm for symbolic integration.
  We show that this data generation method retains the best qualities of
previous data generation methods, while overcoming some of the issues built
into that prior work. The LIOUVILLE generator is able to generate sufficiently
complex and realistic integrands, and could be used for benchmarking or machine
learning training tasks related to symbolic integration.
