An approach for mutation testing using elitist genetic algorithm

Mutation Testing is used as fault-based testing to overcome limitations of other testing approaches but it is recognized as expensive process. In mutation testing, a good test case is one that kills one or more mutants, by producing different mutant output from the original program. Evolutionary algorithms have been proved its suitability for reducing the cost of data generation in different testing methodologies. In order to reduce the cost of mutation testing, efficient test cases are generated that reveal faults and kill mutants. In this paper, we develop a new strategy for generating efficient test input data in the context of mutation testing.