A Fitness Function for Modular Evolutionary Testing of Object-Oriented Programs

We show that encapsulation of states in object-oriented programs hinders the search for test data using evolutionary testing. In a well-designed object-oriented program the encapsulated or hidden state is accessible only through exported or public methods. As client code is oblivious to the internal state of a server object, no guidance is available to test the client code using evolutionary testing. In particular, it is difficult to determine the fitness or goodness of test data, as it may depend on the hidden internal state. However, evolutionary testing is a promising new approach whose effectiveness has been shown by several researchers. We propose a specification-based fitness function for evolutionary testing of object-oriented programs. Our approach is modular in that fitness value calculation doesn\u27t depend on source code of server classes, thus it still works even if the server implementation is changed or no code is available----which is frequently the case for reusable object-oriented class libraries and frameworks. Our approach works for both black-box and white-box based testing