Estimating the feasibility of transition paths in extended finite state machines
Automated Software Engineering
Black-box system testing of real-time embedded systems using random and search-based testing
ICTSS'10 Proceedings of the 22nd IFIP WG 6.1 international conference on Testing software and systems
Transition coverage testing for simulink/stateflow models using messy genetic algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A multi-objective evolutionary algorithm to obtain test cases with variable lengths
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Information and Software Technology
Why the virtual nature of software makes it ideal for search based optimization
FASE'10 Proceedings of the 13th international conference on Fundamental Approaches to Software Engineering
Proceedings of the 2012 International Symposium on Software Testing and Analysis
AUSTIN: An open source tool for search based software testing of C programs
Information and Software Technology
An improved test generation approach from extended finite state machines using genetic algorithms
SEFM'12 Proceedings of the 10th international conference on Software Engineering and Formal Methods
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
UML behavioral model based test case generation: a survey
ACM SIGSOFT Software Engineering Notes
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The application of metaheuristic search techniques in test data generation has been extensively investigated in recent years. Most studies, however, have concentrated on the application of such techniques in structural testing. The use of search-based techniques in functional testing is less frequent, the main cause being the implicit nature of the specification. This paper investigates the use of search-based techniques for functional testing, having the specification in form of a state machine. Its purpose is to generate input data for chosen paths in a state machine, so that the parameter values provided to the methods satisfy the corresponding guards and trigger the desired transitions. A general form of a fitness function for an individual path is presented and this approach is empirically evaluated using three search techniques: simulated annealing, genetic algorithms and particle swarm optimization.