  The automated generation of test code can reduce the time and effort required
to build software while increasing its correctness and robustness. In this
paper, we present RE-ASSERT, an approach for the automated generation of JUnit
test asserts which produces more accurate asserts than previous work with fewer
constraints. This is achieved by targeting projects individually, using precise
code-to-test traceability for learning and by generating assert statements from
the method-under-test directly without the need to write an assert-less test
first. We also utilise Reformer, a state-of-the-art deep learning model, along
with two models from previous work to evaluate ReAssert and an existing
approach, known as ATLAS, using lexical accuracy,uniqueness, and dynamic
analysis. Our evaluation of ReAssert shows up to 44% of generated asserts for a
single project match exactly with the ground truth, increasing to 51% for
generated asserts that compile. We also improve on the ATLAS results through
our use of Reformer with 28% of generated asserts matching exactly with the
ground truth. Reformer also produces the greatest proportion of unique asserts
(71%), giving further evidence that Reformer produces the most useful asserts.
