An enhanced test case selection approach for model-based testing: an industrial case study
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Reducing the cost of model-based testing through test case diversity
ICTSS'10 Proceedings of the 22nd IFIP WG 6.1 international conference on Testing software and systems
An empirical evaluation of several test-a-few strategies for testing particular conditions
Software—Practice & Experience
Pairwise testing for software product lines: comparison of two approaches
Software Quality Control
Achieving scalable model-based testing through test case diversity
ACM Transactions on Software Engineering and Methodology (TOSEM)
Test case selection for black-box regression testing of database applications
Information and Software Technology
A regression test selection technique for embedded software
ACM Transactions on Embedded Computing Systems (TECS)
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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.