Test case selection in model‐based testing is discussed focusing on the use of a similarity function. Automatically generated test suites usually have redundant test cases. The reason is that test generation algorithms are usually based on structural coverage criteria that are applied exhaustively. These criteria may not be helpful to detect redundant test cases as well as the suites are usually impractical due to the huge number of test cases that can be generated. Both problems are addressed by applying a similarity function. The idea is to keep in the suite the less similar test cases according to a goal that is defined in terms of the intended size of the test suite. The strategy presented is compared with random selection by considering transition‐based and fault‐based coverage. The results show that, in most of the cases, similarity‐based selection can be more effective than random selection when applied to automatically generated test suites. Copyright © 2009 John Wiley & Sons, Ltd.

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Test suite reduction strategies aim to produce a smaller and representative suite that presents the same coverage as the original one but is more cost-effective. In the modelbased testing (MBT) context, reduction is crucial since automatic generation algorithms may blindly produce several similar test cases. In order to define the degree of similarity between test cases, researchers have investigated a number of distance functions. However, there is still little or no knowledge on whether and how they influence on the performance of reduction strategies, particularly when considering MBT practices. This paper investigates the effectiveness of distance functions in the scope of a MBT reduction strategy based on the similarity degree of test cases. We discuss six distance functions and apply them to three empirical studies. The first two studies are controlled experiments focusing on two real-world applications (and real faults) and ten synthetic specifications automatically generated from the configuration of each application (and faults randomly generated). In the third study, we also apply the reduction strategy to two subsequent versions of an industrial application by considering real faults detected. Results show that the choice of a distance function has little influence on the size of the reduced test suite. However, as reduced suites are different depending on the distance function applied, the choice can significantly affect the fault coverage. Moreover, it can also affect the stability of the reduction strategy regarding coverage of different sets of faults on different executions.

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We present a systematic procedure of functional test case generation for feature testing of mobile phone applications. A feature is an increment of functionality, usually with a coherent purpose that is added on top of a basic system. Feature are usually developed and tested separately from the basic system as independent modules. The procedure is based on model-based testing techniques with test cases generated from UML sequence diagrams translated into Labeled Transition Systems (LTSs). A case study is presented to illustrate the application of the procedure. The work is part of a research initiative for automation of test case generation, selection and evaluation of Motorola mobile phone applications.