An Immune Genetic Algorithm for Software Test Data Generation
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
GA-based multiple paths test data generator
Computers and Operations Research
Efficient Software Test Case Generation Using Genetic Algorithm Based Graph Theory
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
Search-based multi-paths test data generation for structure-oriented testing
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
FlagRemover: A testability transformation for transforming loop-assigned flags
ACM Transactions on Software Engineering and Methodology (TOSEM)
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On the basis of determining the feasibility of paths, this paper proposes an evolutionary approach to generating test data for feasible basis path coverage. First, the structure of the program under test is expressed by a control flow graph, and the target paths are encoded into the form of hybrid-coding that efficiently combines the statement label with the outcome of a conditional statement (i.e. T or F). Then, the genetic algorithm is employed to generate test data for multiple paths coverage, and the fitness function of an input data (an individual) takes into account the degree of the execution track matching the target paths. Finally, the proposed approach is applied in several benchmark programs. The experimental results show that the proposed approach cannot only avoid redundant test but also improve the efficiency of test data generation effectively