A Method Combined with Data Flow Constraint for Generating Mutation Test Cases

Abstract

Mutation testing is a powerful method of software testing. As a means of finding faults and uncovering test suite defects, the method of test case generation is crucially important. If the test case can generate efficiently and achieve a higher mutation score, it has the potential to help mutation testing be widely adopted. At present, most of the mutation test case generation methods are based on the analysis of the control flow graph, using only the control flow constraint between the statements to guide the generation of test cases, without considering the influence of the data flow constraint among the statements. In this paper, a mutation test case generation method combined with data flow constraint is proposed. First, the control flow constraint and the data flow constraint were combined to model the fitness function; second, the model was used in genetic algorithms to guide the selection and evolution of the test cases. Experiments show that the average iteration number is reduced by 60.53% when generating the same scale test set compared to HGA, and the generated test cases get a 27.9% higher mutation score than random method, showing a better error detection ability.