Abstract
Test suite optimization is mandatory activity to cope with the limited resources and economical constraints. Aside from conventional optimization approaches, computational intelligence based approaches have also focused this research area and evolutionary algorithms have been successful to remarkably reduce the test suite size. These approaches classify the decision space by generating an optimal front called Pareto front. Despite the tremendous research work on interpretation of Pareto front, its visualization and usefulness for higher objectives is a challenging task. Pareto fronts contain multiple suitable solutions that can be converged using heuristics to a reduced set of possible solutions. We have proposed fuzzy based optimization approach in combination with all path coverage criterion to safely reduce a test suite to a single solution. We have initially implemented our proposed work on a testing problem having three objectives; however, it is scalable to any finite number of optimization objectives. We validated our approach by comparing it with evolutionary algorithms. We found that our approach significantly reduces the test suite to a precise test suite. We have concluded that our approach is capable to be automated and provide ‘on the fly’ optimal solution.