Evotec: Evolving the Best Testing Strategy for Contract-Equipped Programs

Automated random testing is efficient 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 article, we 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. Our experiment tested 92 classes over 1710 hours. It shows that Evotec detected 29% more faults than random+ and 18% more faults than the precondition-satisfaction strategy.