Generating Test Data for Both Paths Coverage and Faults Detection Using Genetic Algorithms

Various studies on generating test data have been done up to date, but few test data generated by these studies can effectively detect faults lying in the program. We focus on the problem of generating test data for both paths coverage and faults detection. First, the problem above is formulated as a bi-objective optimization problem with one constraint, whose two objectives are the number of faults detected in the traversed path and the risk level of these faults, respectively, and the unique constraint is that the traversed path is just the target one; then, a multi-objective evolutionary algorithm is employed to effectively solve the formulated model; finally, the proposed method is applied in bubble sort program manually injected with some faults, and compared with the random method and the evolutionary optimization one without the task of detecting faults. The experimental results confirm the advantage of our method.