Fitness Function Design For Nested If-Else And Function Call Flow Involved Construct In Evolutionary Testing

Evolutionary Testing is a promising technology for the automatic test data generation. It reformulates the test data generation problem into an evolutionary search under the guidance of a fitness function. A well-designed fitness function is crucial to the efficiency and effectiveness of evolutionary search.Many efforts have been directed at the design of fitness function. However, in the presence of the nested If-Else construct, the existing fitness function cannot fully evaluate test data. Since for the nested if-else construct, once test data drive the program execution into the branch that will definitely cause the target to be missed, the program execution will be ended and the fitness will be calculated. In this case, the inner branches of the branch where the divergence happens can not be executed, and thus the satisfaction of the test data on these branches can not be assessed. Inspired by testability transformation, a new fitness function is proposed for the nested If-Else construct with a new term optimism level to record the accumulated branch distances of the test data on the unexecuted branches.When function calls exist in the desired execution trace to the target, the evaluation of the test data on the coverage of these function calls, which should be provided to the evolutionary search, is not captured by the existing fitness function. In this case, the existing fitness function can not fairly evaluate the test data. And the evolutionary search will be hampered or even fail in severe cases. In this thesis, a new term function level is first proposed to incorporate into the existing fitness function. It is applied to evaluate the test data's coverage of function calls along the desired path to the target.Experiments validated the effectiveness of the fitness functions presented in this thesis and demonstrated their efficiency.