Generation of test data using meta heuristic approach

Software testing is of huge importance to development of any software. The prime focus is to minimize the expenses on the testing. In software testing the major problem is generation of test data. Several metaheuristic approaches in this field have become very popular. The aim is to generate the optimum set of test data, which would still not compromise on exhaustive testing of software. Our objective is to generate such efficient test data using genetic algorithm and ant colony optimization for a given software. We have also compared the two approaches of software testing to determine which of these are effective towards generation of test data and constraints if any.