Automated Software Test Data Generation
IEEE Transactions on Software Engineering
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Modern heuristic techniques for combinatorial problems
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GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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IEEE Transactions on Software Engineering
Predicate expression cost functions to guide evolutionary search for test data
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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IEEE Transactions on Software Engineering
Search-based test data generation from stateflow statecharts
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Formal analysis of the effectiveness and predictability of random testing
Proceedings of the 19th international symposium on Software testing and analysis
Transition coverage testing for simulink/stateflow models using messy genetic algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Efficient coverage of parallel and hierarchical stateflow models for test case generation
Software Testing, Verification & Reliability
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Proceedings of the 2013 International Workshop on Joining AcadeMiA and Industry Contributions to testing Automation
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Search-based test-data generation has proved successful for code-level testing but almost no search-based work has been carried out at higher levels of abstraction. In this paper the application of such approaches at the higher levels of abstraction offered by MATLAB/Simulink models is investigated and a wide-ranging framework for test-data generation and management is presented. Model-level analogues of code-level structural coverage criteria are presented and search-based approaches to achieving them are described. The paper also describes the first search-based approach to the generation of mutant-killing test data, addressing a fundamental limitation of mutation testing. Some problems remain whatever the level of abstraction considered. In particular, complexity introduced by the presence of persistent state when generating test sequences is as much a challenge at the Simulink model level as it has been found to be at the code level. The framework addresses this problem. Finally, a flexible approach to test sub-set extraction is presented, allowing testing resources to be deployed effectively and efficiently.