Summary This article presents the techniques and results of a novel model‐based test case generation approach that automatically derives test cases from UML state machines. The main contribution of this article is the fully automated fault‐based test case generation technique together with two empirical case studies derived from industrial use cases. Also, an in‐depth evaluation of different fault‐based test case generation strategies on each of the case studies is given and a comparison with plain random testing is conducted. The test case generation methodology supports a wide range of UML constructs and is grounded on the formal semantics of Back's action systems and the well‐known input–output conformance relation. Mutation operators are employed on the level of the specification to insert faults and generate test cases that will reveal the faults inserted. The effectiveness of this approach is shown and it is discussed how to gain a more expressive test suite by combining cheap but undirected random test case generation with the more expensive but directed mutation‐based technique. Finally, an extensive and critical discussion of the lessons learnt is given as well as a future outlook on the general usefulness and practicability of mutation‐based test case generation. Copyright © 2014 John Wiley & Sons, Ltd.

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Model-based mutation testing (MBMT) is a promising testing methodology that relies on a model of the system under test (SUT) to create test cases. Hence, MBMT is a socalled black-box testing approach. It also is fault based, as it creates test cases that are guaranteed to reveal certain faults: after inserting a fault into the model of the SUT, it looks for a test case revealing this fault. This turns MBMT into one of the most powerful and versatile test case generation approaches available as its tests are able to demonstrate the absence of certain faults, can achieve both, control-flow and data-flow coverage of model elements, and also may include information about the behaviour in the failure case. The latter becomes handy whenever the test execution framework is bound in the number of observations it can make and -as a consequence -has to restrict them. However, this versatility comes at a price: MBMT is computationally expensive. The tool MoMuT::UML 1 is the result of a multi-year research effort to bring MBMT from the academic drawing board to industrial use. In this paper we present the current stable version, share the lessons learnt when applying two generations of MoMuT::UML in an industrial setting, and give an outlook on the upcoming, third, generation.

This work introduces a heuristic-guided branching search algorithm for model-based, mutation-driven test case generation. The algorithm is designed towards the efficient and computationally tractable exploration of discrete, non-deterministic models with huge state spaces. Asynchronous parallel processing is a key feature of the algorithm. The algorithm is inspired by the successful path planning algorithm Rapidly exploring Random Trees (RRT). We adapt RRT in several aspects towards test case generation. Most notably, we introduce parametrized heuristics for start and successor state selection, as well as a mechanism to construct test cases from the data produced during search. We implemented our algorithm in the existing test case generation framework MoMuT. We present an extensive evaluation of our heuristics and parameters based on a diverse set of demanding models obtained in an industrial context. In total we continuously utilized 128 CPU cores on three servers for two weeks to gather the experimental data presented. Using statistical methods we determine which heuristics are performing well on all models. With our new algorithm, we are now able to process models consisting of over 2300 concurrent objects. To our knowledge there is no other mutation driven test case generation tool that is able to process models of this magnitude.

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