Model-based, mutant-based test case generator

The MoMuT tools combine mutation-based test case generation with standard techniques to deliver high quality test suites with an excellent cost/benefit ratio. Heart of this new technology is the concept of fault seeding or mutation. The tools use customizable mutation operators to derive mutated (faulty) models from the original test model. Given a mutant and the original test model, the tools then search for a sequence of inputs and outputs that uncovers any design implementing the mutant instead of the original.

Mutation-based test case generation is the most fine-grained and versatile test generation technique available today. It can not only be used to test functional properties of designs but also to generate tests that detect certain non-functional defects. Finally, it also allows the tools to know exactly which faults are caught by a particular test case and to analyze or extend existing test sets.


  • Automated and model-based test case generation
  • Customizable and very fine grained control over the test coverage via fault models.
  • Assessment and extension of an existing test suite.
  • An optimized test suite for regression testing.
  • Fault location support for failed regression tests
  • Integration into existing workflow via OSLC.

We are in the process of adding support for activity diagrams, integration with Enterprise Architect and model defactoring. Adding a better User Interface.

  • MoMuT Website:
  • MoMuT AIT website:
  • MoMuT Datasheet:   
  • MoMuT Flyer: ttps://   
  • Schlick R., Krenn W. (2019) Tackling the Challenges of Internet-of-Things-Development using Models. 2ndInternational Workshop on Embedded Software for the Industrial IOT, DATE, Florence, 2019.
  • Schlick R., Herzner W., Jöbstl E. (2011) Fault-Based Generation of Test Cases from UML-Models – Approach and Some Experiences. In: Flammini F., Bologna S., Vittorini V. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2011. Lecture Notes in Computer Science, vol 6894. Springer, Berlin, Heidelberg.
  • B. Aichernig, H. Brandl, E. Jöbstl, W. Krenn, R. Schlick, and S. Tiran, “MoMuT::UML model-based mutation testing for UML,” in Software testing, verification and validation (icst), 2015 ieee 8th international conference on, 2015, pp. 1-8.
  • B. K. Aichernig, K. Hörmaier, F. Lorber, D. Ničković, R. Schlick, D. Simoneau, and S. Tiran, “Integration of Requirements Engineering and Test-Case Generation via OSLC,” in Qsic ’14: proceedings of the 2014 14th international conference on quality software, Dallas, USA, 2014, p. 117–126.
  • B. K. Aichernig, E. Jöbstl, and S. Tiran, “Model-based mutation testing via symbolic refinement checking,” Science of computer programming, 2014.
  • B. K. Aichernig, H. Brandl, E. Jöbstl, W. Krenn, R. Schlick, and S. Tiran, “Killing strategies for model-based mutation testing,” Software testing, verification and reliability, p. n/a–n/a, 2014.
  • B. K. Aichernig, J. Auer, E. Jöbstl, R. Korošec, W. Krenn, R. Schlick, and B. V. Schmidt, “Model-based mutation testing of an industrial measurement device,” in Tests and proofs, M. Seidl and N. Tillmann, Eds., Springer International Publishing, 2014, vol. 8570, p. 1–19.
  • W. Krenn, R. Schlick, and B. K. Aichernig, “Mapping UML to labeled transition systems for test-case generation: a translation via object-oriented action systems,” in Proceedings of the 8th international conference on formal methods for components and objects, Berlin, Heidelberg, 2010, p. 186–207.
Relationships with other web-repo artefacts
Improvement Classification
Error Coverage
Coverage of test set
Open source - Goals
Cybersecurity, Safety, Privacy

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