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Automated random testing is effective at detecting faults but it is certainly not an optimal testing strategy for every given program. For example, an automated random testing tool ignores that some routines have stronger preconditions, they use certain literal values, or they are more error-prone. Taking into account such characteristics may increase testing effectiveness. In this paper, the authors present Evotec, an enhancement of random testing which relies on genetic algorithms to evolve a best testing strategy for contract-equipped programs. The resulting strategy is optimized for detecting more faults, satisfying more routine preconditions and establishing more object states on a given set of classes to test.
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