  In the context of black-box testing, generating test cases through model
mutation is known to produce powerful test suites but usually has the drawback
of being prohibitively expensive. This paper presents a new version of the tool
MoMuT::UML (www.momut.org), which implements a scalable version of
mutation-driven test case generation (MDTCG). It is able to handle
industrial-sized UML models comprising networks of, e.g., 2800 interacting
state machines. To achieve the required scalability, the implemented algorithm
exploits the concurrency in MDTCG and combines it with a search based
generation strategy. For evaluation, we use seven case studies of different
application domains with an increasing level of difficulty, stopping at a model
of a railway station in Austria's national rail network.
