Nature-inspired techniques for conformance testing of object-oriented software
Applied Soft Computing
Transition coverage testing for simulink/stateflow models using messy genetic algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Testing and model-checking techniques for diagnosis
TestCom'07/FATES'07 Proceedings of the 19th IFIP TC6/WG6.1 international conference, and 7th international conference on Testing of Software and Communicating Systems
Runtime analysis of the (1+1) EA on computing unique input output sequences
Information Sciences: an International Journal
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When testing from finite state machines, a failure observed in the implementation under test (IUT) is called a symptom. A symptom could have been caused by an earlier state transfer failure. Transitions that may be used to explain the observed symptoms are called diagnosing candidates. Finding strategies to generate an optimal set of diagnosing candidates that could effectively identify faults in the IUT is of great value in reducing the cost of system development and testing. This paper investigates fault diagnosis when testing from finite state machines and proposes heuristics for fault isolation and identification. The proposed heuristics attempt to lead to a symptom being observed in some shorter test sequences, which helps to reduce the cost of fault isolation and identification. The complexity of the proposed method is analysed. A case study is presented, which shows how the proposed approach assists in fault diagnosis. Copyright © 2006 John Wiley & Sons, Ltd.