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Keywords:

  • test case selection;
  • model-based testing;
  • LTS

Abstract

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.