  Model-based testing (MBT) provides an automated approach for finding
discrepancies between software models and their implementation. If we want to
incorporate MBT into the fast and iterative software development process that
is Continuous Integration Continuous Deployment, then MBT must be able to test
the entire model in as little time as possible. However, current academic MBT
tools either traverse models at random, which we show to be ineffective for
this purpose, or use precalculated optimal paths which can not be efficiently
calculated for large industrial models. We provide a new traversal strategy
that provides an improvement in error-detection rate comparable to using
recalculated paths. We show that the new strategy is able to be applied
efficiently to large models. The benchmarks are performed on a mix of
real-world and pseudo-randomly generated models. We observe no significant
difference between these two types of models.
